Dynamic precision management for integer deep learning primitives

ABSTRACT

One embodiment provides for a graphics processing unit to perform computations associated with a neural network, the graphics processing unit comprising compute unit including a hardware logic unit having dynamic precision fixed-point logic, the compute unit to receive a set of dynamic fixed-point tensors, compute, via the dynamic precision fixed-point logic, a right-shift value using an absolute maximum value within the set of dynamic fixed-point tensors and a dynamic range of the set of dynamic fixed-point tensors, right-shift data values within the set of dynamic fixed-point tensors based on the right-shift value, increment a shared exponent associated with the set of dynamic fixed-point tensors based on the right-shift value, perform a compute operation on the set of dynamic fixed-point tensors, and generate an output tensor via the compute operation on the set of dynamic fixed-point tensors.

CROSS-REFERENCE

This application is a divisional of and claims priority to U.S.application Ser. No. 15/881,991, filed Jan. 29, 2018, issued as U.S.Pat. No. 10,643,297, which claims the priority to U.S. ProvisionalPatent Application No. 62/501,796, filed on May 5, 2017, which is herebyincorporated herein by reference.

FIELD

Embodiments relate generally to data processing and more particularly todata processing via a general-purpose graphics processing unit.

BACKGROUND OF THE DESCRIPTION

Current parallel graphics data processing includes systems and methodsdeveloped to perform specific operations on graphics data such as, forexample, linear interpolation, tessellation, rasterization, texturemapping, depth testing, etc. Traditionally, graphics processors usedfixed function computational units to process graphics data; however,more recently, portions of graphics processors have been madeprogrammable, enabling such processors to support a wider variety ofoperations for processing vertex and fragment data.

To further increase performance, graphics processors typically implementprocessing techniques such as pipelining that attempt to process, inparallel, as much graphics data as possible throughout the differentparts of the graphics pipeline. Parallel graphics processors with singleinstruction, multiple thread (SIMT) architectures are designed tomaximize the amount of parallel processing in the graphics pipeline. Inan SIMT architecture, groups of parallel threads attempt to executeprogram instructions synchronously together as often as possible toincrease processing efficiency. A general overview of software andhardware for SIMT architectures can be found in Shane Cook, CUDAProgramming, Chapter 3, pages 37-51 (2013) and/or Nicholas Wilt, CUDAHandbook, A Comprehensive Guide to GPU Programming, Sections 2.6.2 to3.1.2 (June 2013).

BRIEF DESCRIPTION OF THE DRAWINGS

So that the features of the present invention can be understood indetail, a more particular description of the invention may be had byreference to embodiments, some of which are illustrated in the appendeddrawings. It is to be noted, however, that the appended drawingsillustrate only typical embodiments and are therefore not to beconsidered limiting of the scope of all embodiments.

FIG. 1 is a block diagram illustrating a computer system configured toimplement one or more aspects of the embodiments described herein;

FIG. 2A-2D illustrate parallel processor components, according to anembodiment;

FIG. 3A-3B are block diagrams of graphics multiprocessors, according toembodiments;

FIG. 4A-4F illustrate an exemplary architecture in which a plurality ofGPUs is communicatively coupled to a plurality of multi-core processors;

FIG. 5 illustrates a graphics processing pipeline, according to anembodiment;

FIG. 6 illustrates a machine learning software stack, according to anembodiment;

FIG. 7 illustrates a highly-parallel general-purpose graphics processingunit, according to an embodiment;

FIG. 8 illustrates a multi-GPU computing system, according to anembodiment;

FIG. 9A-9B illustrate layers of exemplary deep neural networks;

FIG. 10 illustrates an exemplary recurrent neural network;

FIG. 11 illustrates training and deployment of a deep neural network;

FIG. 12 is a block diagram illustrating distributed learning;

FIG. 13 illustrates an exemplary inferencing system on a chip (SOC)suitable for performing inferencing using a trained model;

FIG. 14 illustrates a comparison between floating-point and a dynamicfixed-point format, according to an embodiment;

FIG. 15A illustrates dynamic fixed-point quantization, according to anembodiment;

FIG. 15B illustrates de-quantization, according to an embodiment;

FIG. 16A illustrates equations for arithmetic operations and downconversion according to embodiments described herein;

FIG. 16B illustrates equations for hardware accelerated dynamicfixed-point primitives, according to an embodiment;

FIG. 17 illustrates floating-point to dynamic fixed-point biasedrounding, according to an embodiment;

FIG. 18 is a block diagram of a multiprocessor unit, according to anembodiment;

FIG. 19A-19B illustrates a logic unit that can be configured withdynamic precision capability, according to an embodiment;

FIG. 20 is a flow diagram illustrating logic to enable dynamic precisionmanagement for integer deep learning primitives, according to anembodiment;

FIG. 21A-21D illustrate blocked dynamic fixed-point and low-precisionfloating-point data, according to embodiments described herein;

FIG. 22 is a block diagram of a processing system, according to anembodiment;

FIG. 23 is a block diagram of a processor according to an embodiment;

FIG. 24 is a block diagram of a graphics processor, according to anembodiment;

FIG. 25 is a block diagram of a graphics processing engine of a graphicsprocessor in accordance with some embodiments;

FIG. 26 is a block diagram of a graphics processor provided by anadditional embodiment;

FIG. 27 illustrates thread execution logic including an array ofprocessing elements employed in some embodiments;

FIG. 28 is a block diagram illustrating a graphics processor instructionformats according to some embodiments;

FIG. 29 is a block diagram of a graphics processor according to anotherembodiment;

FIG. 30A-30B illustrate a graphics processor command format and commandsequence, according to some embodiments;

FIG. 31 illustrates exemplary graphics software architecture for a dataprocessing system according to some embodiments;

FIG. 32 is a block diagram illustrating an IP core development system,according to an embodiment;

FIG. 33 is a block diagram illustrating an exemplary system on a chipintegrated circuit, according to an embodiment;

FIG. 34 is a block diagram illustrating an additional graphicsprocessor, according to an embodiment; and

FIG. 35 is a block diagram illustrating an additional exemplary graphicsprocessor of a system on a chip integrated circuit, according to anembodiment.

DETAILED DESCRIPTION

In some embodiments, a graphics processing unit (GPU) is communicativelycoupled to host/processor cores to accelerate graphics operations,machine-learning operations, pattern analysis operations, and variousgeneral-purpose GPU (GPGPU) functions. The GPU may be communicativelycoupled to the host processor/cores over a bus or another interconnect(e.g., a high-speed interconnect such as PCIe or NVLink). In otherembodiments, the GPU may be integrated on the same package or chip asthe cores and communicatively coupled to the cores over an internalprocessor bus/interconnect (i.e., internal to the package or chip).Regardless of the manner in which the GPU is connected, the processorcores may allocate work to the GPU in the form of sequences ofcommands/instructions contained in a work descriptor. The GPU then usesdedicated circuitry/logic for efficiently processing thesecommands/instructions.

In the following description, numerous specific details are set forth toprovide a more thorough understanding. However, it will be apparent toone of skill in the art that the embodiments described herein may bepracticed without one or more of these specific details. In otherinstances, well-known features have not been described to avoidobscuring the details of the present embodiments.

System Overview

FIG. 1 is a block diagram illustrating a computing system 100 configuredto implement one or more aspects of the embodiments described herein.The computing system 100 includes a processing subsystem 101 having oneor more processor(s) 102 and a system memory 104 communicating via aninterconnection path that may include a memory hub 105. The memory hub105 may be a separate component within a chipset component or may beintegrated within the one or more processor(s) 102. The memory hub 105couples with an I/O subsystem 111 via a communication link 106. The I/Osubsystem 111 includes an I/O hub 107 that can enable the computingsystem 100 to receive input from one or more input device(s) 108.Additionally, the I/O hub 107 can enable a display controller, which maybe included in the one or more processor(s) 102, to provide outputs toone or more display device(s) 110A. In one embodiment the one or moredisplay device(s) 110A coupled with the I/O hub 107 can include a local,internal, or embedded display device.

In one embodiment the processing subsystem 101 includes one or moreparallel processor(s) 112 coupled to memory hub 105 via a bus or othercommunication link 113. The communication link 113 may be one of anynumber of standards-based communication link technologies or protocols,such as, but not limited to PCI Express, or may be a vendor specificcommunications interface or communications fabric. In one embodiment theone or more parallel processor(s) 112 form a computationally focusedparallel or vector processing system that can include a large number ofprocessing cores and/or processing clusters, such as a many integratedcore (MIC) processor. In one embodiment the one or more parallelprocessor(s) 112 form a graphics processing subsystem that can outputpixels to one of the one or more display device(s) 110A coupled via theI/O Hub 107. The one or more parallel processor(s) 112 can also includea display controller and display interface (not shown) to enable adirect connection to one or more display device(s) 110B.

Within the I/O subsystem 111, a system storage unit 114 can connect tothe I/O hub 107 to provide a storage mechanism for the computing system100. An I/O switch 116 can be used to provide an interface mechanism toenable connections between the I/O hub 107 and other components, such asa network adapter 118 and/or wireless network adapter 119 that may beintegrated into the platform, and various other devices that can beadded via one or more add-in device(s) 120. The network adapter 118 canbe an Ethernet adapter or another wired network adapter. The wirelessnetwork adapter 119 can include one or more of a Wi-Fi, Bluetooth, nearfield communication (NFC), or other network device that includes one ormore wireless radios.

The computing system 100 can include other components not explicitlyshown, including USB or other port connections, optical storage drives,video capture devices, and the like, may also be connected to the I/Ohub 107. Communication paths interconnecting the various components inFIG. 1 may be implemented using any suitable protocols, such as PCI(Peripheral Component Interconnect) based protocols (e.g., PCI-Express),or any other bus or point-to-point communication interfaces and/orprotocol(s), such as the NV-Link high-speed interconnect, orinterconnect protocols known in the art.

In one embodiment, the one or more parallel processor(s) 112 incorporatecircuitry optimized for graphics and video processing, including, forexample, video output circuitry, and constitutes a graphics processingunit (GPU). In another embodiment, the one or more parallel processor(s)112 incorporate circuitry optimized for general purpose processing,while preserving the underlying computational architecture, described ingreater detail herein. In yet another embodiment, components of thecomputing system 100 may be integrated with one or more other systemelements on a single integrated circuit. For example, the one or moreparallel processor(s) 112, memory hub 105, processor(s) 102, and I/O hub107 can be integrated into a system on chip (SoC) integrated circuit.Alternatively, the components of the computing system 100 can beintegrated into a single package to form a system in package (SIP)configuration. In one embodiment, at least a portion of the componentsof the computing system 100 can be integrated into a multi-chip module(MCM), which can be interconnected with other multi-chip modules into amodular computing system.

It will be appreciated that the computing system 100 shown herein isillustrative and that variations and modifications are possible. Theconnection topology, including the number and arrangement of bridges,the number of processor(s) 102, and the number of parallel processor(s)112, may be modified as desired. For instance, in some embodiments,system memory 104 is connected to the processor(s) 102 directly ratherthan through a bridge, while other devices communicate with systemmemory 104 via the memory hub 105 and the processor(s) 102. In otheralternative topologies, the parallel processor(s) 112 are connected tothe I/O hub 107 or directly to one of the one or more processor(s) 102,rather than to the memory hub 105. In other embodiments, the I/O hub 107and memory hub 105 may be integrated into a single chip. Someembodiments may include two or more sets of processor(s) 102 attachedvia multiple sockets, which can couple with two or more instances of theparallel processor(s) 112.

Some of the particular components shown herein are optional and may notbe included in all implementations of the computing system 100. Forexample, any number of add-in cards or peripherals may be supported, orsome components may be eliminated. Furthermore, some architectures mayuse different terminology for components similar to those illustrated inFIG. 1. For example, the memory hub 105 may be referred to as aNorthbridge in some architectures, while the I/O hub 107 may be referredto as a Southbridge.

FIG. 2A illustrates a parallel processor 200, according to anembodiment. The various components of the parallel processor 200 may beimplemented using one or more integrated circuit devices, such asprogrammable processors, application specific integrated circuits(ASICs), or field programmable gate arrays (FPGA). The illustratedparallel processor 200 is a variant of the one or more parallelprocessor(s) 112 shown in FIG. 1, according to an embodiment.

In one embodiment the parallel processor 200 includes a parallelprocessing unit 202. The parallel processing unit includes an I/O unit204 that enables communication with other devices, including otherinstances of the parallel processing unit 202. The I/O unit 204 may bedirectly connected to other devices. In one embodiment the I/O unit 204connects with other devices via the use of a hub or switch interface,such as memory hub 105. The connections between the memory hub 105 andthe I/O unit 204 form a communication link 113. Within the parallelprocessing unit 202, the I/O unit 204 connects with a host interface 206and a memory crossbar 216, where the host interface 206 receivescommands directed to performing processing operations and the memorycrossbar 216 receives commands directed to performing memory operations.

When the host interface 206 receives a command buffer via the I/O unit204, the host interface 206 can direct work operations to perform thosecommands to a front end 208. In one embodiment the front end 208 coupleswith a scheduler 210, which is configured to distribute commands orother work items to a processing cluster array 212. In one embodimentthe scheduler 210 ensures that the processing cluster array 212 isproperly configured and in a valid state before tasks are distributed tothe processing clusters of the processing cluster array 212. In oneembodiment the scheduler 210 is implemented via firmware logic executingon a microcontroller. The microcontroller implemented scheduler 210 isconfigurable to perform complex scheduling and work distributionoperations at coarse and fine granularity, enabling rapid preemption andcontext switching of threads executing on the processing array 212. Inone embodiment, the host software can prove workloads for scheduling onthe processing array 212 via one of multiple graphics processingdoorbells. The workloads can then be automatically distributed acrossthe processing array 212 by the scheduler 210 logic within the schedulermicrocontroller.

The processing cluster array 212 can include up to “N” processingclusters (e.g., cluster 214A, cluster 214B, through cluster 214N). Eachcluster 214A-214N of the processing cluster array 212 can execute alarge number of concurrent threads. The scheduler 210 can allocate workto the clusters 214A-214N of the processing cluster array 212 usingvarious scheduling and/or work distribution algorithms, which may varydepending on the workload arising for each type of program orcomputation. The scheduling can be handled dynamically by the scheduler210, or can be assisted in part by compiler logic during compilation ofprogram logic configured for execution by the processing cluster array212. In one embodiment, different clusters 214A-214N of the processingcluster array 212 can be allocated for processing different types ofprograms or for performing different types of computations.

The processing cluster array 212 can be configured to perform varioustypes of parallel processing operations. In one embodiment theprocessing cluster array 212 is configured to perform general-purposeparallel compute operations. For example, the processing cluster array212 can include logic to execute processing tasks including filtering ofvideo and/or audio data, performing modeling operations, includingphysics operations, and performing data transformations.

In one embodiment the processing cluster array 212 is configured toperform parallel graphics processing operations. In embodiments in whichthe parallel processor 200 is configured to perform graphics processingoperations, the processing cluster array 212 can include additionallogic to support the execution of such graphics processing operations,including, but not limited to texture sampling logic to perform textureoperations, as well as tessellation logic and other vertex processinglogic. Additionally, the processing cluster array 212 can be configuredto execute graphics processing related shader programs such as, but notlimited to vertex shaders, tessellation shaders, geometry shaders, andpixel shaders. The parallel processing unit 202 can transfer data fromsystem memory via the I/O unit 204 for processing. During processing thetransferred data can be stored to on-chip memory (e.g., parallelprocessor memory 222) during processing, then written back to systemmemory.

In one embodiment, when the parallel processing unit 202 is used toperform graphics processing, the scheduler 210 can be configured todivide the processing workload into approximately equal sized tasks, tobetter enable distribution of the graphics processing operations tomultiple clusters 214A-214N of the processing cluster array 212. In someembodiments, portions of the processing cluster array 212 can beconfigured to perform different types of processing. For example, afirst portion may be configured to perform vertex shading and topologygeneration, a second portion may be configured to perform tessellationand geometry shading, and a third portion may be configured to performpixel shading or other screen space operations, to produce a renderedimage for display. Intermediate data produced by one or more of theclusters 214A-214N may be stored in buffers to allow the intermediatedata to be transmitted between clusters 214A-214N for furtherprocessing.

During operation, the processing cluster array 212 can receiveprocessing tasks to be executed via the scheduler 210, which receivescommands defining processing tasks from front end 208. For graphicsprocessing operations, processing tasks can include indices of data tobe processed, e.g., surface (patch) data, primitive data, vertex data,and/or pixel data, as well as state parameters and commands defining howthe data is to be processed (e.g., what program is to be executed). Thescheduler 210 may be configured to fetch the indices corresponding tothe tasks or may receive the indices from the front end 208. The frontend 208 can be configured to ensure the processing cluster array 212 isconfigured to a valid state before the workload specified by incomingcommand buffers (e.g., batch-buffers, push buffers, etc.) is initiated.

Each of the one or more instances of the parallel processing unit 202can couple with parallel processor memory 222. The parallel processormemory 222 can be accessed via the memory crossbar 216, which canreceive memory requests from the processing cluster array 212 as well asthe I/O unit 204. The memory crossbar 216 can access the parallelprocessor memory 222 via a memory interface 218. The memory interface218 can include multiple partition units (e.g., partition unit 220A,partition unit 220B, through partition unit 220N) that can each coupleto a portion (e.g., memory unit) of parallel processor memory 222. Inone implementation, the number of partition units 220A-220N isconfigured to be equal to the number of memory units, such that a firstpartition unit 220A has a corresponding first memory unit 224A, a secondpartition unit 220B has a corresponding memory unit 224B, and an Nthpartition unit 220N has a corresponding Nth memory unit 224N. In otherembodiments, the number of partition units 220A-220N may not be equal tothe number of memory devices.

In various embodiments, the memory units 224A-224N can include varioustypes of memory devices, including dynamic random-access memory (DRAM)or graphics random access memory, such as synchronous graphics randomaccess memory (SGRAM), including graphics double data rate (GDDR)memory. In one embodiment, the memory units 224A-224N may also include3D stacked memory, including but not limited to high bandwidth memory(HBM). Persons skilled in the art will appreciate that the specificimplementation of the memory units 224A-224N can vary, and can beselected from one of various conventional designs. Render targets, suchas frame buffers or texture maps may be stored across the memory units224A-224N, allowing partition units 220A-220N to write portions of eachrender target in parallel to efficiently use the available bandwidth ofparallel processor memory 222. In some embodiments, a local instance ofthe parallel processor memory 222 may be excluded in favor of a unifiedmemory design that utilizes system memory in conjunction with localcache memory.

In one embodiment, any one of the clusters 214A-214N of the processingcluster array 212 can process data that will be written to any of thememory units 224A-224N within parallel processor memory 222. The memorycrossbar 216 can be configured to transfer the output of each cluster214A-214N to any partition unit 220A-220N or to another cluster214A-214N, which can perform additional processing operations on theoutput. Each cluster 214A-214N can communicate with the memory interface218 through the memory crossbar 216 to read from or write to variousexternal memory devices. In one embodiment, the memory crossbar 216 hasa connection to the memory interface 218 to communicate with the I/Ounit 204, as well as a connection to a local instance of the parallelprocessor memory 222, enabling the processing units within the differentprocessing clusters 214A-214N to communicate with system memory or othermemory that is not local to the parallel processing unit 202. In oneembodiment, the memory crossbar 216 can use virtual channels to separatetraffic streams between the clusters 214A-214N and the partition units220A-220N.

While a single instance of the parallel processing unit 202 isillustrated within the parallel processor 200, any number of instancesof the parallel processing unit 202 can be included. For example,multiple instances of the parallel processing unit 202 can be providedon a single add-in card, or multiple add-in cards can be interconnected.The different instances of the parallel processing unit 202 can beconfigured to inter-operate even if the different instances havedifferent numbers of processing cores, different amounts of localparallel processor memory, and/or other configuration differences. Forexample, in one embodiment some instances of the parallel processingunit 202 can include higher precision floating-point units relative toother instances. Systems incorporating one or more instances of theparallel processing unit 202 or the parallel processor 200 can beimplemented in a variety of configurations and form factors, includingbut not limited to desktop, laptop, or handheld personal computers,servers, workstations, game consoles, and/or embedded systems.

FIG. 2B is a block diagram of a partition unit 220, according to anembodiment. In one embodiment, the partition unit 220 is an instance ofone of the partition units 220A-220N of FIG. 2A. As illustrated, thepartition unit 220 includes an L2 cache 221, a frame buffer interface225, and a ROP 226 (raster operations unit). The L2 cache 221 is aread/write cache that is configured to perform load and store operationsreceived from the memory crossbar 216 and ROP 226. Read misses andurgent write-back requests are output by L2 cache 221 to frame bufferinterface 225 for processing. Updates can also be sent to the framebuffer via the frame buffer interface 225 for processing. In oneembodiment the frame buffer interface 225 interfaces with one of thememory units in parallel processor memory, such as the memory units224A-224N of FIG. 2A (e.g., within parallel processor memory 222).

In graphics applications, the ROP 226 is a processing unit that performsraster operations such as stencil, z test, blending, and the like. TheROP 226 then outputs processed graphics data that is stored in graphicsmemory. In some embodiments, the ROP 226 includes compression logic tocompress depth or color data that is written to memory and decompressdepth or color data that is read from memory. The compression logic canbe lossless compression logic that makes use of one or more of multiplecompression algorithms. The type of compression that is performed by theROP 226 can vary based on the statistical characteristics of the data tobe compressed. For example, in one embodiment, delta color compressionis performed on depth and color data on a per-tile basis.

In some embodiments, the ROP 226 is included within each processingcluster (e.g., cluster 214A-214N of FIG. 2A) instead of within thepartition unit 220. In such embodiment, read and write requests forpixel data are transmitted over the memory crossbar 216 instead of pixelfragment data. The processed graphics data may be displayed on a displaydevice, such as one of the one or more display device(s) 110 of FIG. 1,routed for further processing by the processor(s) 102, or routed forfurther processing by one of the processing entities within the parallelprocessor 200 of FIG. 2A.

FIG. 2C is a block diagram of a processing cluster 214 within a parallelprocessing unit, according to an embodiment. In one embodiment, theprocessing cluster is an instance of one of the processing clusters214A-214N of FIG. 2A. The processing cluster 214 can be configured toexecute many threads in parallel, where the term “thread” refers to aninstance of a particular program executing on a particular set of inputdata. In some embodiments, single-instruction, multiple-data (SIMD)instruction issue techniques are used to support parallel execution of alarge number of threads without providing multiple independentinstruction units. In other embodiments, single-instruction,multiple-thread (SIMT) techniques are used to support parallel executionof a large number of generally synchronized threads, using a commoninstruction unit configured to issue instructions to a set of processingengines within each one of the processing clusters. Unlike a SIMDexecution regime, where all processing engines typically executeidentical instructions, SIMT execution allows different threads to morereadily follow divergent execution paths through a given thread program.Persons skilled in the art will understand that a SIMD processing regimerepresents a functional subset of a SIMT processing regime.

Operation of the processing cluster 214 can be controlled via a pipelinemanager 232 that distributes processing tasks to SIMT parallelprocessors. The pipeline manager 232 receives instructions from thescheduler 210 of FIG. 2A Fig. and manages execution of thoseinstructions via a graphics multiprocessor 234 and/or a texture unit236. The illustrated graphics multiprocessor 234 is an exemplaryinstance of a SIMT parallel processor. However, various types of SIMTparallel processors of differing architectures may be included withinthe processing cluster 214. One or more instances of the graphicsmultiprocessor 234 can be included within a processing cluster 214. Thegraphics multiprocessor 234 can process data and a data crossbar 240 canbe used to distribute the processed data to one of multiple possibledestinations, including other shader units. The pipeline manager 232 canfacilitate the distribution of processed data by specifying destinationsfor processed data to be distributed via the data crossbar 240.

Each graphics multiprocessor 234 within the processing cluster 214 caninclude an identical set of functional execution logic (e.g., arithmeticlogic units, load-store units, etc.). The functional execution logic canbe configured in a pipelined manner in which new instructions can beissued before previous instructions are complete. The functionalexecution logic supports a variety of operations including integer andfloating-point arithmetic, comparison operations, Boolean operations,bit-shifting, and computation of various algebraic functions. In oneembodiment, the same functional-unit hardware can be leveraged toperform different operations and any combination of functional units maybe present.

The instructions transmitted to the processing cluster 214 constitutes athread. A set of threads executing across the set of parallel processingengines is a thread group. A thread group executes the same program ondifferent input data. Each thread within a thread group can be assignedto a different processing engine within a graphics multiprocessor 234. Athread group may include fewer threads than the number of processingengines within the graphics multiprocessor 234. When a thread groupincludes fewer threads than the number of processing engines, one ormore of the processing engines may be idle during cycles in which thatthread group is being processed. A thread group may also include morethreads than the number of processing engines within the graphicsmultiprocessor 234. When the thread group includes more threads than thenumber of processing engines within the graphics multiprocessor 234,processing can be performed over consecutive clock cycles. In oneembodiment, multiple thread groups can be executed concurrently on agraphics multiprocessor 234.

In one embodiment, the graphics multiprocessor 234 includes an internalcache memory to perform load and store operations. In one embodiment,the graphics multiprocessor 234 can forego an internal cache and use acache memory (e.g., L1 cache 248) within the processing cluster 214.Each graphics multiprocessor 234 also has access to L2 caches within thepartition units (e.g., partition units 220A-220N of FIG. 2A) that areshared among all processing clusters 214 and may be used to transferdata between threads. The graphics multiprocessor 234 may also accessoff-chip global memory, which can include one or more of local parallelprocessor memory and/or system memory. Any memory external to theparallel processing unit 202 may be used as global memory. Embodimentsin which the processing cluster 214 includes multiple instances of thegraphics multiprocessor 234 can share common instructions and data,which may be stored in the L1 cache 248.

Each processing cluster 214 may include an MMU 245 (memory managementunit) that is configured to map virtual addresses into physicaladdresses. In other embodiments, one or more instances of the MMU 245may reside within the memory interface 218 of FIG. 2A. The MMU 245includes a set of page table entries (PTEs) used to map a virtualaddress to a physical address of a tile and optionally a cache lineindex. The MMU 245 may include address translation lookaside buffers(TLB) or caches that may reside within the graphics multiprocessor 234or the L1 cache or processing cluster 214. The physical address isprocessed to distribute surface data access locality to allow efficientrequest interleaving among partition units. The cache line index may beused to determine whether a request for a cache line is a hit or miss.

In graphics and computing applications, a processing cluster 214 may beconfigured such that each graphics multiprocessor 234 is coupled to atexture unit 236 for performing texture mapping operations, e.g.,determining texture sample positions, reading texture data, andfiltering the texture data. Texture data is read from an internaltexture L1 cache (not shown) or in some embodiments from the L1 cachewithin graphics multiprocessor 234 and is fetched from an L2 cache,local parallel processor memory, or system memory, as needed. Eachgraphics multiprocessor 234 outputs processed tasks to the data crossbar240 to provide the processed task to another processing cluster 214 forfurther processing or to store the processed task in an L2 cache, localparallel processor memory, or system memory via the memory crossbar 216.A preROP 242 (pre-raster operations unit) is configured to receive datafrom graphics multiprocessor 234, direct data to ROP units, which may belocated with partition units as described herein (e.g., partition units220A-220N of FIG. 2A). The preROP 242 unit can perform optimizations forcolor blending, organize pixel color data, and perform addresstranslations.

It will be appreciated that the core architecture described herein isillustrative and that variations and modifications are possible. Anynumber of processing units, e.g., graphics multiprocessor 234, textureunits 236, preROPs 242, etc., may be included within a processingcluster 214. Further, while only one processing cluster 214 is shown, aparallel processing unit as described herein may include any number ofinstances of the processing cluster 214. In one embodiment, eachprocessing cluster 214 can be configured to operate independently ofother processing clusters 214 using separate and distinct processingunits, L1 caches, etc.

FIG. 2D shows a graphics multiprocessor 234, according to oneembodiment. In such embodiment, the graphics multiprocessor 234 coupleswith the pipeline manager 232 of the processing cluster 214. Thegraphics multiprocessor 234 has an execution pipeline including but notlimited to an instruction cache 252, an instruction unit 254, an addressmapping unit 256, a register file 258, one or more general purposegraphics processing unit (GPGPU) cores 262, and one or more load/storeunits 266. The GPGPU cores 262 and load/store units 266 are coupled withcache memory 272 and shared memory 270 via a memory and cacheinterconnect 268.

In one embodiment, the instruction cache 252 receives a stream ofinstructions to execute from the pipeline manager 232. The instructionsare cached in the instruction cache 252 and dispatched for execution bythe instruction unit 254. The instruction unit 254 can dispatchinstructions as thread groups (e.g., warps), with each thread of thethread group assigned to a different execution unit within GPGPU core262. An instruction can access any of a local, shared, or global addressspace by specifying an address within a unified address space. Theaddress mapping unit 256 can be used to translate addresses in theunified address space into a distinct memory address that can beaccessed by the load/store units 266.

The register file 258 provides a set of registers for the functionalunits of the graphics multiprocessor 234. The register file 258 providestemporary storage for operands connected to the data paths of thefunctional units (e.g., GPGPU cores 262, load/store units 266) of thegraphics multiprocessor 234. In one embodiment, the register file 258 isdivided between each of the functional units such that each functionalunit is allocated a dedicated portion of the register file 258. In oneembodiment, the register file 258 is divided between the different warpsbeing executed by the graphics multiprocessor 234.

The GPGPU cores 262 can each include floating-point units (FPUs) and/orinteger arithmetic logic units (ALUs) that are used to executeinstructions of the graphics multiprocessor 234. The GPGPU cores 262 canbe similar in architecture or can differ in architecture, according toembodiments. For example, in one embodiment, a first portion of theGPGPU cores 262 include a single precision FPU and an integer ALU whilea second portion of the GPGPU cores include a double precision FPU. Inone embodiment, the FPUs can implement the IEEE 754-2008 standard forfloating-point arithmetic or enable variable precision floating-pointarithmetic. The graphics multiprocessor 234 can additionally include oneor more fixed function or special function units to perform specificfunctions such as copy rectangle or pixel blending operations. In oneembodiment one or more of the GPGPU cores can also include fixed orspecial function logic.

In one embodiment, the GPGPU cores 262 include SIMD logic capable ofperforming a single instruction on multiple sets of data. In oneembodiment GPGPU cores 262 can physically execute SIMD4, SIMD8, andSIMD16 instructions and logically execute SIMD1, SIMD2, and SIMD32instructions. The SIMD instructions for the GPGPU cores can be generatedat compile time by a shader compiler or automatically generated whenexecuting programs written and compiled for single program multiple data(SPMD) or SIMT architectures. Multiple threads of a program configuredfor the SIMT execution model can be executed via a single SIMDinstruction. For example and in one embodiment, eight SIMT threads thatperform the same or similar operations can be executed in parallel via asingle SIMD8 logic unit.

The memory and cache interconnect 268 is an interconnect network thatconnects each of the functional units of the graphics multiprocessor 234to the register file 258 and to the shared memory 270. In oneembodiment, the memory and cache interconnect 268 is a crossbarinterconnect that allows the load/store unit 266 to implement load andstore operations between the shared memory 270 and the register file258. The register file 258 can operate at the same frequency as theGPGPU cores 262, thus data transfer between the GPGPU cores 262 and theregister file 258 is very low latency. The shared memory 270 can be usedto enable communication between threads that execute on the functionalunits within the graphics multiprocessor 234. The cache memory 272 canbe used as a data cache for example, to cache texture data communicatedbetween the functional units and the texture unit 236. The shared memory270 can also be used as a program managed cached. Threads executing onthe GPGPU cores 262 can programmatically store data within the sharedmemory in addition to the automatically cached data that is storedwithin the cache memory 272.

FIG. 3A-3B illustrate additional graphics multiprocessors, according toembodiments. The illustrated graphics multiprocessors 325, 350 arevariants of the graphics multiprocessor 234 of FIG. 2C. The illustratedgraphics multiprocessors 325, 350 can be configured as a streamingmultiprocessor (SM) capable of simultaneous execution of a large numberof execution threads.

FIG. 3A shows a graphics multiprocessor 325 according to an additionalembodiment. The graphics multiprocessor 325 includes multiple additionalinstances of execution resource units relative to the graphicsmultiprocessor 234 of FIG. 2D. For example, the graphics multiprocessor325 can include multiple instances of the instruction unit 332A-332B,register file 334A-334B, and texture unit(s) 344A-344B. The graphicsmultiprocessor 325 also includes multiple sets of graphics or computeexecution units (e.g., GPGPU core 336A-336B, GPGPU core 337A-337B, GPGPUcore 338A-338B) and multiple sets of load/store units 340A-340B. In oneembodiment the execution resource units have a common instruction cache330, texture and/or data cache memory 342, and shared memory 346.

The various components can communicate via an interconnect fabric 327.In one embodiment the interconnect fabric 327 includes one or morecrossbar switches to enable communication between the various componentsof the graphics multiprocessor 325. In one embodiment the interconnectfabric 327 is a separate, high-speed network fabric layer upon whicheach component of the graphics multiprocessor 325 is stacked. Thecomponents of the graphics multiprocessor 325 communicate with remotecomponents via the interconnect fabric 327. For example, the GPGPU cores336A-336B, 337A-337B, and 3378A-338B can each communicate with sharedmemory 346 via the interconnect fabric 327. The interconnect fabric 327can arbitrate communication within the graphics multiprocessor 325 toensure a fair bandwidth allocation between components.

FIG. 3B shows a graphics multiprocessor 350 according to an additionalembodiment. The graphics processor includes multiple sets of executionresources 356A-356D, where each set of execution resource includesmultiple instruction units, register files, GPGPU cores, and load storeunits, as illustrated in FIG. 2D and FIG. 3A. The execution resources356A-356D can work in concert with texture unit(s) 360A-360D for textureoperations, while sharing an instruction cache 354, and shared memory353. In one embodiment the execution resources 356A-356D can share aninstruction cache 354 and shared memory 353, as well as multipleinstances of a texture and/or data cache memory 358A-358B. The variouscomponents can communicate via an interconnect fabric 352 similar to theinterconnect fabric 327 of FIG. 3A.

Persons skilled in the art will understand that the architecturedescribed in FIGS. 1, 2A-2D, and 3A-3B are descriptive and not limitingas to the scope of the present embodiments. Thus, the techniquesdescribed herein may be implemented on any properly configuredprocessing unit, including, without limitation, one or more mobileapplication processors, one or more desktop or server central processingunits (CPUs) including multi-core CPUs, one or more parallel processingunits, such as the parallel processing unit 202 of FIG. 2A, as well asone or more graphics processors or special purpose processing units,without departure from the scope of the embodiments described herein.

In some embodiments a parallel processor or GPGPU as described herein iscommunicatively coupled to host/processor cores to accelerate graphicsoperations, machine-learning operations, pattern analysis operations,and various general purpose GPU (GPGPU) functions. The GPU may becommunicatively coupled to the host processor/cores over a bus or otherinterconnect (e.g., a high-speed interconnect such as PCIe or NVLink).In other embodiments, the GPU may be integrated on the same package orchip as the cores and communicatively coupled to the cores over aninternal processor bus/interconnect (i.e., internal to the package orchip). Regardless of the manner in which the GPU is connected, theprocessor cores may allocate work to the GPU in the form of sequences ofcommands/instructions contained in a work descriptor. The GPU then usesdedicated circuitry/logic for efficiently processing thesecommands/instructions.

Techniques for GPU to Host Processor Interconnection

FIG. 4A illustrates an exemplary architecture in which a plurality ofGPUs 410-413 is communicatively coupled to a plurality of multi-coreprocessors 405-406 over high-speed links 440A-440D (e.g., buses,point-to-point interconnects, etc.). In one embodiment, the high-speedlinks 440A-440D support a communication throughput of 4 GB/s, 30 GB/s,80 GB/s or higher, depending on the implementation. Various interconnectprotocols may be used including, but not limited to, PCIe 4.0 or 5.0 andNVLink (e.g., NVLink 1.0, NVLink 2.0, or another Nvidia high-speedsignaling interconnect (NVHS)). However, the underlying principles ofthe invention are not limited to any particular communication protocolor throughput.

In addition, in one embodiment, two or more of the GPUs 410-413 areinterconnected over high-speed links 442A-442B, which may be implementedusing the same or different protocols/links than those used forhigh-speed links 440A-440D. Similarly, two or more of the multi-coreprocessors 405-406 may be connected over high speed link 443 which maybe symmetric multi-processor (SMP) buses operating at 20 GB/s, 30 GB/s,120 GB/s or higher. Alternatively, all communication between the varioussystem components shown in FIG. 4A may be accomplished using the sameprotocols/links (e.g., over a common interconnection fabric). Asmentioned, however, the underlying principles of the invention are notlimited to any particular type of interconnect technology.

In one embodiment, each multi-core processor 405-406 is communicativelycoupled to a processor memory 401-402, via memory interconnects430A-430B, respectively, and each GPU 410-413 is communicatively coupledto GPU memory 420-423 over GPU memory interconnects 450A-450D,respectively. The memory interconnects 430A-430B and 450A-450D mayutilize the same or different memory access technologies. By way ofexample, and not limitation, the processor memories 401-402 and GPUmemories 420-423 may be volatile memories such as dynamic random accessmemories (DRAMs) (including stacked DRAMs), Graphics DDR SDRAM (GDDR)(e.g., GDDR5, GDDR6), or High Bandwidth Memory (HBM) and/or may benon-volatile memories such as 3D XPoint or Nano-Ram. In one embodiment,some portion of the memories may be volatile memory and another portionmay be non-volatile memory (e.g., using a two-level memory (2LM)hierarchy).

As described below, although the various processors 405-406 and GPUs410-413 may be physically coupled to a particular memory 401-402,420-423, respectively, a unified memory architecture may be implementedin which the same virtual system address space (also referred to as the“effective address” space) is distributed among all of the variousphysical memories. For example, processor memories 401-402 may eachcomprise 64 GB of the system memory address space and GPU memories420-423 may each comprise 32 GB of the system memory address space(resulting in a total of 256 GB addressable memory in this example).

FIG. 4B illustrates additional details for an interconnection between amulti-core processor 407 and a graphics acceleration module 446 inaccordance with one embodiment. The graphics acceleration module 446 mayinclude one or more GPU chips integrated on a line card which is coupledto the processor 407 via the high-speed link 440. Alternatively, thegraphics acceleration module 446 may be integrated on the same packageor chip as the processor 407.

The illustrated processor 407 includes a plurality of cores 460A-460D,each with a translation lookaside buffer 461A-461D and one or morecaches 462A-462D. The cores may include various other components forexecuting instructions and processing data which are not illustrated toavoid obscuring the underlying principles of the invention (e.g.,instruction fetch units, branch prediction units, decoders, executionunits, reorder buffers, etc.). The caches 462A-462D may comprise level 1(L1) and level 2 (L2) caches. In addition, one or more shared caches 456may be included in the caching hierarchy and shared by sets of the cores460A-460D. For example, one embodiment of the processor 407 includes 24cores, each with its own L1 cache, twelve shared L2 caches, and twelveshared L3 caches. In this embodiment, one of the L2 and L3 caches areshared by two adjacent cores. The processor 407 and the graphicsaccelerator integration module 446 connect with system memory 441, whichmay include processor memories 401-402.

Coherency is maintained for data and instructions stored in the variouscaches 462A-462D, 456 and system memory 441 via inter-core communicationover a coherence bus 464. For example, each cache may have cachecoherency logic/circuitry associated therewith to communicate to overthe coherence bus 464 in response to detected reads or writes toparticular cache lines. In one implementation, a cache snooping protocolis implemented over the coherence bus 464 to snoop cache accesses. Cachesnooping/coherency techniques are well understood by those of skill inthe art and will not be described in detail here to avoid obscuring theunderlying principles of the invention.

In one embodiment, a proxy circuit 425 communicatively couples thegraphics acceleration module 446 to the coherence bus 464, allowing thegraphics acceleration module 446 to participate in the cache coherenceprotocol as a peer of the cores. In particular, an interface 435provides connectivity to the proxy circuit 425 over high-speed link 440(e.g., a PCIe bus, NVLink, etc.) and an interface 437 connects thegraphics acceleration module 446 to the high-speed link 440.

In one implementation, an accelerator integration circuit 436 providescache management, memory access, context management, and interruptmanagement services on behalf of a plurality of graphics processingengines 431, 432, N of the graphics acceleration module 446. Thegraphics processing engines 431, 432, N may each comprise a separategraphics processing unit (GPU). Alternatively, the graphics processingengines 431, 432, N may comprise different types of graphics processingengines within a GPU such as graphics execution units, media processingengines (e.g., video encoders/decoders), samplers, and blit engines. Inother words, the graphics acceleration module may be a GPU with aplurality of graphics processing engines 431-432, N or the graphicsprocessing engines 431-432, N may be individual GPUs integrated on acommon package, line card, or chip.

In one embodiment, the accelerator integration circuit 436 includes amemory management unit (MMU) 439 for performing various memorymanagement functions such as virtual-to-physical memory translations(also referred to as effective-to-real memory translations) and memoryaccess protocols for accessing system memory 441. The MMU 439 may alsoinclude a translation lookaside buffer (TLB) (not shown) for caching thevirtual/effective to physical/real address translations. In oneembodiment, the accelerator integration circuit 436 includes a fetchunit 491 to fetch commands, instructions, work descriptors, etc., thatdefine operations to be performed. In one implementation, a cache 438stores commands and data for efficient access by the graphics processingengines 431-432, N. In one embodiment, the data stored in cache 438 andgraphics memories 433-434, M is kept coherent with the core caches462A-462D, 456 and system memory 411. As mentioned, this may beaccomplished via proxy circuit 425 which takes part in the cachecoherency mechanism on behalf of cache 438 and memories 433-434, M(e.g., sending updates to the cache 438 related tomodifications/accesses of cache lines on processor caches 462A-462D, 456and receiving updates from the cache 438).

A set of registers 445 store context data for threads executed by thegraphics processing engines 431-432, N and a context management circuit448 manages the thread contexts. For example, the context managementcircuit 448 may perform save and restore operations to save and restorecontexts of the various threads during contexts switches (e.g., where afirst thread is saved and a second thread is stored so that the secondthread can be execute by a graphics processing engine). For example, ona context switch, the context management circuit 448 may store currentregister values to a designated region in memory (e.g., identified by acontext pointer). It may then restore the register values when returningto the context. In one embodiment, an interrupt management circuit 447receives and processes interrupts received from system devices.

In one implementation, virtual/effective addresses from a graphicsprocessing engine 431 are translated to real/physical addresses insystem memory 411 by the MMU 439. One embodiment of the acceleratorintegration circuit 436 supports multiple (e.g., 4, 8, 16) graphicsaccelerator modules 446 and/or other accelerator devices. The graphicsaccelerator module 446 may be dedicated to a single application executedon the processor 407 or may be shared between multiple applications. Inone embodiment, a virtualized graphics execution environment ispresented in which the resources of the graphics processing engines431-432, N are shared with multiple applications or virtual machines(VMs). The resources may be subdivided into “slices” which are allocatedto different VMs and/or applications based on the processingrequirements and priorities associated with the VMs and/or applications.

Thus, the accelerator integration circuit acts as a bridge to the systemfor the graphics acceleration module 446 and provides addresstranslation and system memory cache services. In addition, theaccelerator integration circuit 436 may provide virtualizationfacilities for the host processor to manage virtualization of thegraphics processing engines, interrupts, and memory management.

Because hardware resources of the graphics processing engines 431-432, Nare mapped explicitly to the real address space seen by the hostprocessor 407, any host processor can address these resources directlyusing an effective address value. One function of the acceleratorintegration circuit 436, in one embodiment, is the physical separationof the graphics processing engines 431-432, N so that they appear to thesystem as independent units.

As mentioned, in the illustrated embodiment, one or more graphicsmemories 433-434, M are coupled to each of the graphics processingengines 431-432, N, respectively. The graphics memories 433-434, M storeinstructions and data being processed by each of the graphics processingengines 431-432, N. The graphics memories 433-434, M may be volatilememories such as DRAMs (including stacked DRAMs), GDDR memory (e.g.,GDDR5, GDDR6), or HBM, and/or may be non-volatile memories such as 3DXPoint or Nano-Ram.

In one embodiment, to reduce data traffic over the high-speed link 440,biasing techniques are used to ensure that the data stored in graphicsmemories 433-434, M is data which will be used most frequently by thegraphics processing engines 431-432, N and preferably not used by thecores 460A-460D (at least not frequently). Similarly, the biasingmechanism attempts to keep data needed by the cores (and preferably notthe graphics processing engines 431-432, N) within the caches 462A-462D,456 of the cores and system memory 411.

FIG. 4C illustrates another embodiment in which the acceleratorintegration circuit 436 is integrated within the processor 407. In thisembodiment, the graphics processing engines 431-432, N communicatedirectly over the high-speed link 440 to the accelerator integrationcircuit 436 via interface 437 and interface 435 (which, again, may beutilize any form of bus or interface protocol). The acceleratorintegration circuit 436 may perform the same operations as thosedescribed with respect to FIG. 4B, but potentially at a higherthroughput given its close proximity to the coherency bus 464 and caches462A-462D, 456.

One embodiment supports different programming models including adedicated-process programming model (no graphics acceleration modulevirtualization) and shared programming models (with virtualization). Thelatter may include programming models which are controlled by theaccelerator integration circuit 436 and programming models which arecontrolled by the graphics acceleration module 446.

In one embodiment of the dedicated process model, graphics processingengines 431-432, N are dedicated to a single application or processunder a single operating system. The single application can funnel otherapplication requests to the graphics engines 431-432, N, providingvirtualization within a VM/partition.

In the dedicated-process programming models, the graphics processingengines 431-432, N, may be shared by multiple VM/application partitions.The shared models require a system hypervisor to virtualize the graphicsprocessing engines 431-432, N to allow access by each operating system.For single-partition systems without a hypervisor, the graphicsprocessing engines 431-432, N are owned by the operating system. In bothcases, the operating system can virtualize the graphics processingengines 431-432, N to provide access to each process or application.

For the shared programming model, the graphics acceleration module 446or an individual graphics processing engine 431-432, N selects a processelement using a process handle. In one embodiment, process elements arestored in system memory 411 and are addressable using the effectiveaddress to real address translation techniques described herein. Theprocess handle may be an implementation-specific value provided to thehost process when registering its context with the graphics processingengine 431-432, N (that is, calling system software to add the processelement to the process element linked list). The lower 16-bits of theprocess handle may be the offset of the process element within theprocess element linked list.

FIG. 4D illustrates an exemplary accelerator integration slice 490. Asused herein, a “slice” comprises a specified portion of the processingresources of the accelerator integration circuit 436. Applicationeffective address space 482 within system memory 411 stores processelements 483. In one embodiment, the process elements 483 are stored inresponse to GPU invocations 481 from applications 480 executed on theprocessor 407. A process element 483 contains the process state for thecorresponding application 480. A work descriptor (WD) 484 contained inthe process element 483 can be a single job requested by an applicationor may contain a pointer to a queue of jobs. In the latter case, the WD484 is a pointer to the job request queue in the application's addressspace 482.

The graphics acceleration module 446 and/or the individual graphicsprocessing engines 431-432, N can be shared by all or a subset of theprocesses in the system. Embodiments of the invention include aninfrastructure for setting up the process state and sending a WD 484 toa graphics acceleration module 446 to start a job in a virtualizedenvironment.

In one implementation, the dedicated-process programming model isimplementation-specific. In this model, a single process owns thegraphics acceleration module 446 or an individual graphics processingengine 431. Because the graphics acceleration module 446 is owned by asingle process, the hypervisor initializes the accelerator integrationcircuit 436 for the owning partition and the operating systeminitializes the accelerator integration circuit 436 for the owningprocess at the time when the graphics acceleration module 446 isassigned.

In operation, a WD fetch unit 491 in the accelerator integration slice490 fetches the next WD 484 which includes an indication of the work tobe done by one of the graphics processing engines of the graphicsacceleration module 446. Data from the WD 484 may be stored in registers445 and used by the MMU 439, interrupt management circuit 447 and/orcontext management circuit 448 as illustrated. For example, oneembodiment of the MMU 439 includes segment/page walk circuitry foraccessing segment/page tables 486 within the OS virtual address space485. The interrupt management circuit 447 may process interrupt events492 received from the graphics acceleration module 446. When performinggraphics operations, an effective address 493 generated by a graphicsprocessing engine 431-432, N is translated to a real address by the MMU439.

In one embodiment, the same set of registers 445 are duplicated for eachgraphics processing engine 431-432, N and/or graphics accelerationmodule 446 and may be initialized by the hypervisor or operating system.Each of these duplicated registers may be included in an acceleratorintegration slice 490. Exemplary registers that may be initialized bythe hypervisor are shown in Table 1.

TABLE 1 Hypervisor Initialized Registers 1 Slice Control Register 2 RealAddress (RA) Scheduled Processes Area Pointer 3 Authority Mask OverrideRegister 4 Interrupt Vector Table Entry Offset 5 Interrupt Vector TableEntry Limit 6 State Register 7 Logical Partition ID 8 Real address (RA)Hypervisor Accelerator Utilization Record Pointer 9 Storage DescriptionRegister

Exemplary registers that may be initialized by the operating system areshown in Table 2.

TABLE 2 Operating System Initialized Registers 1 Process and ThreadIdentification 2 Effective Address (EA) Context Save/Restore Pointer 3Virtual Address (VA) Accelerator Utilization Record Pointer 4 VirtualAddress (VA) Storage Segment Table Pointer 5 Authority Mask 6 Workdescriptor

In one embodiment, each WD 484 is specific to a particular graphicsacceleration module 446 and/or graphics processing engine 431-432, N. Itcontains all the information a graphics processing engine 431-432, Nrequires to do its work or it can be a pointer to a memory locationwhere the application has set up a command queue of work to becompleted.

FIG. 4E illustrates additional details for one embodiment of a sharedmodel. This embodiment includes a hypervisor real address space 498 inwhich a process element list 499 is stored. The hypervisor real addressspace 498 is accessible via a hypervisor 496 which virtualizes thegraphics acceleration module engines for the operating system 495.

The shared programming models allow for all or a subset of processesfrom all or a subset of partitions in the system to use a graphicsacceleration module 446. There are two programming models where thegraphics acceleration module 446 is shared by multiple processes andpartitions: time-sliced shared and graphics directed shared.

In this model, the system hypervisor 496 owns the graphics accelerationmodule 446 and makes its function available to all operating systems495. For a graphics acceleration module 446 to support virtualization bythe system hypervisor 496, the graphics acceleration module 446 mayadhere to the following requirements: 1) An application's job requestmust be autonomous (that is, the state does not need to be maintainedbetween jobs), or the graphics acceleration module 446 must provide acontext save and restore mechanism. 2) An application's job request isguaranteed by the graphics acceleration module 446 to complete in aspecified amount of time, including any translation faults, or thegraphics acceleration module 446 provides the ability to preempt theprocessing of the job. 3) The graphics acceleration module 446 must beguaranteed fairness between processes when operating in the directedshared programming model.

In one embodiment, for the shared model, the application 480 is requiredto make an operating system 495 system call with a graphics accelerationmodule 446 type, a work descriptor (WD), an authority mask register(AMR) value, and a context save/restore area pointer (CSRP). Thegraphics acceleration module 446 type describes the targetedacceleration function for the system call. The graphics accelerationmodule 446 type may be a system-specific value. The WD is formattedspecifically for the graphics acceleration module 446 and can be in theform of a graphics acceleration module 446 command, an effective addresspointer to a user-defined structure, an effective address pointer to aqueue of commands, or any other data structure to describe the work tobe done by the graphics acceleration module 446. In one embodiment, theAMR value is the AMR state to use for the current process. The valuepassed to the operating system is similar to an application setting theAMR. If the accelerator integration circuit 436 and graphicsacceleration module 446 implementations do not support a User AuthorityMask Override Register (UAMOR), the operating system may apply thecurrent UAMOR value to the AMR value before passing the AMR in thehypervisor call. The hypervisor 496 may optionally apply the currentAuthority Mask Override Register (AMOR) value before placing the AMRinto the process element 483. In one embodiment, the CSRP is one of theregisters 445 containing the effective address of an area in theapplication's address space 482 for the graphics acceleration module 446to save and restore the context state. This pointer is optional if nostate is required to be saved between jobs or when a job is preempted.The context save/restore area may be pinned system memory.

Upon receiving the system call, the operating system 495 may verify thatthe application 480 has registered and been given the authority to usethe graphics acceleration module 446. The operating system 495 thencalls the hypervisor 496 with the information shown in Table 3.

TABLE 3 OS to Hypervisor Call Parameters 1 A work descriptor (WD) 2 AnAuthority Mask Register (AMR) value (potentially masked). 3 An effectiveaddress (EA) Context Save/Restore Area Pointer (CSRP) 4 A process ID(PID) and optional thread ID (TID) 5 A virtual address (VA) acceleratorutilization record pointer (AURP) 6 The virtual address of the storagesegment table pointer (SSTP) 7 A logical interrupt service number (LISN)

Upon receiving the hypervisor call, the hypervisor 496 verifies that theoperating system 495 has registered and been given the authority to usethe graphics acceleration module 446. The hypervisor 496 then puts theprocess element 483 into the process element linked list for thecorresponding graphics acceleration module 446 type. The process elementmay include the information shown in Table 4.

TABLE 4 Process Element Information 1 A work descriptor (WD) 2 AnAuthority Mask Register (AMR) value (potentially masked). 3 An effectiveaddress (EA) Context Save/Restore Area Pointer (CSRP) 4 A process ID(PID) and optional thread ID (TID) 5 A virtual address (VA) acceleratorutilization record pointer (AURP) 6 The virtual address of the storagesegment table pointer (SSTP) 7 A logical interrupt service number (LISN)8 Interrupt vector table, derived from the hypervisor call parameters. 9A state register (SR) value 10 A logical partition ID (LPID) 11 A realaddress (RA) hypervisor accelerator utilization record pointer 12 TheStorage Descriptor Register (SDR)

In one embodiment, the hypervisor initializes a plurality of acceleratorintegration slice 490 registers 445.

As illustrated in FIG. 4F, one embodiment of the invention employs aunified memory addressable via a common virtual memory address spaceused to access the physical processor memories 401-402 and GPU memories420-423. In this implementation, operations executed on the GPUs 410-413utilize the same virtual/effective memory address space to access theprocessors memories 401-402 and vice versa, thereby simplifyingprogrammability. In one embodiment, a first portion of thevirtual/effective address space is allocated to the processor memory401, a second portion to the second processor memory 402, a thirdportion to the GPU memory 420, and so on. The entire virtual/effectivememory space (sometimes referred to as the effective address space) isthereby distributed across each of the processor memories 401-402 andGPU memories 420-423, allowing any processor or GPU to access anyphysical memory with a virtual address mapped to that memory.

In one embodiment, bias/coherence management circuitry 494A-494E withinone or more of the MMUs 439A-439E ensures cache coherence between thecaches of the host processors (e.g., 405) and the GPUs 410-413 andimplements biasing techniques indicating the physical memories in whichcertain types of data should be stored. While multiple instances ofbias/coherence management circuitry 494A-494E are illustrated in FIG.4F, the bias/coherence circuitry may be implemented within the MMU ofone or more host processors 405 and/or within the acceleratorintegration circuit 436.

One embodiment allows GPU-attached memory 420-423 to be mapped as partof system memory, and accessed using shared virtual memory (SVM)technology, but without suffering the typical performance drawbacksassociated with full system cache coherence. The ability to GPU-attachedmemory 420-423 to be accessed as system memory without onerous cachecoherence overhead provides a beneficial operating environment for GPUoffload. This arrangement allows the host processor 405 software tosetup operands and access computation results, without the overhead oftradition I/O DMA data copies. Such traditional copies involve drivercalls, interrupts and memory mapped I/O (MMIO) accesses that are allinefficient relative to simple memory accesses. At the same time, theability to access GPU attached memory 420-423 without cache coherenceoverheads can be critical to the execution time of an offloadedcomputation. In cases with substantial streaming write memory traffic,for example, cache coherence overhead can significantly reduce theeffective write bandwidth seen by a GPU 410-413. The efficiency ofoperand setup, the efficiency of results access, and the efficiency ofGPU computation all play a role in determining the effectiveness of GPUoffload.

In one implementation, the selection of between GPU bias and hostprocessor bias is driven by a bias tracker data structure. A bias tablemay be used, for example, which may be a page-granular structure (i.e.,controlled at the granularity of a memory page) that includes 1 or 2bits per GPU-attached memory page. The bias table may be implemented ina stolen memory range of one or more GPU-attached memories 420-423, withor without a bias cache in the GPU 410-413 (e.g., to cachefrequently/recently used entries of the bias table). Alternatively, theentire bias table may be maintained within the GPU.

In one implementation, the bias table entry associated with each accessto the GPU-attached memory 420-423 is accessed prior the actual accessto the GPU memory, causing the following operations. First, localrequests from the GPU 410-413 that find their page in GPU bias areforwarded directly to a corresponding GPU memory 420-423. Local requestsfrom the GPU that find their page in host bias are forwarded to theprocessor 405 (e.g., over a high-speed link as discussed above). In oneembodiment, requests from the processor 405 that find the requested pagein host processor bias complete the request like a normal memory read.Alternatively, requests directed to a GPU-biased page may be forwardedto the GPU 410-413. The GPU may then transition the page to a hostprocessor bias if it is not currently using the page.

The bias state of a page can be changed either by a software-basedmechanism, a hardware-assisted software-based mechanism, or, for alimited set of cases, a purely hardware-based mechanism.

One mechanism for changing the bias state employs an API call (e.g.OpenCL), which, in turn, calls the GPU's device driver which, in turn,sends a message (or enqueues a command descriptor) to the GPU directingit to change the bias state and, for some transitions, perform a cacheflushing operation in the host. The cache flushing operation is requiredfor a transition from host processor 405 bias to GPU bias, but is notrequired for the opposite transition.

In one embodiment, cache coherency is maintained by temporarilyrendering GPU-biased pages uncacheable by the host processor 405. Toaccess these pages, the processor 405 may request access from the GPU410 which may or may not grant access right away, depending on theimplementation. Thus, to reduce communication between the host processor405 and GPU 410 it is beneficial to ensure that GPU-biased pages arethose which are required by the GPU but not the host processor 405 andvice versa.

Graphics Processing Pipeline

FIG. 5 illustrates a graphics processing pipeline 500, according to anembodiment. In one embodiment a graphics processor can implement theillustrated graphics processing pipeline 500. The graphics processor canbe included within the parallel processing subsystems as describedherein, such as the parallel processor 200 of FIG. 2A, which, in oneembodiment, is a variant of the parallel processor(s) 112 of FIG. 1. Thevarious parallel processing systems can implement the graphicsprocessing pipeline 500 via one or more instances of the parallelprocessing unit (e.g., parallel processing unit 202 of FIG. 2A) asdescribed herein. For example, a shader unit (e.g., graphicsmultiprocessor 234 of FIG. 2C) may be configured to perform thefunctions of one or more of a vertex processing unit 504, a tessellationcontrol processing unit 508, a tessellation evaluation processing unit512, a geometry processing unit 516, and a fragment/pixel processingunit 524. The functions of data assembler 502, primitive assemblers 506,514, 518, tessellation unit 510, rasterizer 522, and raster operationsunit 526 may also be performed by other processing engines within aprocessing cluster (e.g., processing cluster 214 of FIG. 2A) and acorresponding partition unit (e.g., partition unit 220A-220N of FIG.2A). The graphics processing pipeline 500 may also be implemented usingdedicated processing units for one or more functions. In one embodiment,one or more portions of the graphics processing pipeline 500 can beperformed by parallel processing logic within a general purposeprocessor (e.g., CPU). In one embodiment, one or more portions of thegraphics processing pipeline 500 can access on-chip memory (e.g.,parallel processor memory 222 as in FIG. 2A) via a memory interface 528,which may be an instance of the memory interface 218 of FIG. 2A.

In one embodiment the data assembler 502 is a processing unit thatcollects vertex data for surfaces and primitives. The data assembler 502then outputs the vertex data, including the vertex attributes, to thevertex processing unit 504. The vertex processing unit 504 is aprogrammable execution unit that executes vertex shader programs,lighting and transforming vertex data as specified by the vertex shaderprograms. The vertex processing unit 504 reads data that is stored incache, local or system memory for use in processing the vertex data andmay be programmed to transform the vertex data from an object-basedcoordinate representation to a world space coordinate space or anormalized device coordinate space.

A first instance of a primitive assembler 506 receives vertex attributesfrom the vertex processing unit 504. The primitive assembler 506readings stored vertex attributes as needed and constructs graphicsprimitives for processing by tessellation control processing unit 508.The graphics primitives include triangles, line segments, points,patches, and so forth, as supported by various graphics processingapplication programming interfaces (APIs).

The tessellation control processing unit 508 treats the input verticesas control points for a geometric patch. The control points aretransformed from an input representation from the patch (e.g., thepatch's bases) to a representation that is suitable for use in surfaceevaluation by the tessellation evaluation processing unit 512. Thetessellation control processing unit 508 can also compute tessellationfactors for edges of geometric patches. A tessellation factor applies toa single edge and quantifies a view-dependent level of detail associatedwith the edge. A tessellation unit 510 is configured to receive thetessellation factors for edges of a patch and to tessellate the patchinto multiple geometric primitives such as line, triangle, orquadrilateral primitives, which are transmitted to a tessellationevaluation processing unit 512. The tessellation evaluation processingunit 512 operates on parameterized coordinates of the subdivided patchto generate a surface representation and vertex attributes for eachvertex associated with the geometric primitives.

A second instance of a primitive assembler 514 receives vertexattributes from the tessellation evaluation processing unit 512, readingstored vertex attributes as needed, and constructs graphics primitivesfor processing by the geometry processing unit 516. The geometryprocessing unit 516 is a programmable execution unit that executesgeometry shader programs to transform graphics primitives received fromprimitive assembler 514 as specified by the geometry shader programs. Inone embodiment the geometry processing unit 516 is programmed tosubdivide the graphics primitives into one or more new graphicsprimitives and calculate parameters used to rasterize the new graphicsprimitives.

In some embodiments the geometry processing unit 516 can add or deleteelements in the geometry stream. The geometry processing unit 516outputs the parameters and vertices specifying new graphics primitivesto primitive assembler 518. The primitive assembler 518 receives theparameters and vertices from the geometry processing unit 516 andconstructs graphics primitives for processing by a viewport scale, cull,and clip unit 520. The geometry processing unit 516 reads data that isstored in parallel processor memory or system memory for use inprocessing the geometry data. The viewport scale, cull, and clip unit520 performs clipping, culling, and viewport scaling and outputsprocessed graphics primitives to a rasterizer 522.

The rasterizer 522 can perform depth culling and other depth-basedoptimizations. The rasterizer 522 also performs scan conversion on thenew graphics primitives to generate fragments and output those fragmentsand associated coverage data to the fragment/pixel processing unit 524.The fragment/pixel processing unit 524 is a programmable execution unitthat is configured to execute fragment shader programs or pixel shaderprograms. The fragment/pixel processing unit 524 transforming fragmentsor pixels received from rasterizer 522, as specified by the fragment orpixel shader programs. For example, the fragment/pixel processing unit524 may be programmed to perform operations included but not limited totexture mapping, shading, blending, texture correction and perspectivecorrection to produce shaded fragments or pixels that are output to araster operations unit 526. The fragment/pixel processing unit 524 canread data that is stored in either the parallel processor memory or thesystem memory for use when processing the fragment data. Fragment orpixel shader programs may be configured to shade at sample, pixel, tile,or other granularities depending on the sampling rate configured for theprocessing units.

The raster operations unit 526 is a processing unit that performs rasteroperations including, but not limited to stencil, z-test, blending, andthe like, and outputs pixel data as processed graphics data to be storedin graphics memory (e.g., parallel processor memory 222 as in FIG. 2A,and/or system memory 104 as in FIG. 1), to be displayed on the one ormore display device(s) 110 or for further processing by one of the oneor more processor(s) 102 or parallel processor(s) 112. In someembodiments the raster operations unit 526 is configured to compress zor color data that is written to memory and decompress z or color datathat is read from memory.

Machine Learning Overview

A machine learning algorithm is an algorithm that can learn based on aset of data. Embodiments of machine learning algorithms can be designedto model high-level abstractions within a data set. For example, imagerecognition algorithms can be used to determine which of severalcategories to which a given input belong; regression algorithms canoutput a numerical value given an input; and pattern recognitionalgorithms can be used to generate translated text or perform text tospeech and/or speech recognition.

An exemplary type of machine learning algorithm is a neural network.There are many types of neural networks; a simple type of neural networkis a feedforward network. A feedforward network may be implemented as anacyclic graph in which the nodes are arranged in layers. Typically, afeedforward network topology includes an input layer and an output layerthat are separated by at least one hidden layer. The hidden layertransforms input received by the input layer into a representation thatis useful for generating output in the output layer. The network nodesare fully connected via edges to the nodes in adjacent layers, but thereare no edges between nodes within each layer. Data received at the nodesof an input layer of a feedforward network are propagated (i.e., “fedforward”) to the nodes of the output layer via an activation functionthat calculates the states of the nodes of each successive layer in thenetwork based on coefficients (“weights”) respectively associated witheach of the edges connecting the layers. Depending on the specific modelbeing represented by the algorithm being executed, the output from theneural network algorithm can take various forms.

Before a machine learning algorithm can be used to model a particularproblem, the algorithm is trained using a training data set. Training aneural network involves selecting a network topology, using a set oftraining data representing a problem being modeled by the network, andadjusting the weights until the network model performs with a minimalerror for all instances of the training data set. For example, during asupervised learning training process for a neural network, the outputproduced by the network in response to the input representing aninstance in a training data set is compared to the “correct” labeledoutput for that instance, an error signal representing the differencebetween the output and the labeled output is calculated, and the weightsassociated with the connections are adjusted to minimize that error asthe error signal is backward propagated through the layers of thenetwork. The network is considered “trained” when the errors for each ofthe outputs generated from the instances of the training data set areminimized.

The accuracy of a machine learning algorithm can be affectedsignificantly by the quality of the data set used to train thealgorithm. The training process can be computationally intensive and mayrequire a significant amount of time on a conventional general-purposeprocessor. Accordingly, parallel processing hardware is used to trainmany types of machine learning algorithms. This is particularly usefulfor optimizing the training of neural networks, as the computationsperformed in adjusting the coefficients in neural networks lendthemselves naturally to parallel implementations. Specifically, manymachine learning algorithms and software applications have been adaptedto make use of the parallel processing hardware within general-purposegraphics processing devices.

FIG. 6 is a generalized diagram of a machine learning software stack600. A machine learning application 602 can be configured to train aneural network using a training dataset or to use a trained deep neuralnetwork to implement machine intelligence. The machine learningapplication 602 can include training and inference functionality for aneural network and/or specialized software that can be used to train aneural network before deployment. The machine learning application 602can implement any type of machine intelligence including but not limitedto image recognition, mapping and localization, autonomous navigation,speech synthesis, medical imaging, or language translation.

Hardware acceleration for the machine learning application 602 can beenabled via a machine learning framework 604. The machine learningframework 604 can provide a library of machine learning primitives.Machine learning primitives are basic operations that are commonlyperformed by machine learning algorithms. Without the machine learningframework 604, developers of machine learning algorithms would berequired to create and optimize the main computational logic associatedwith the machine learning algorithm, then re-optimize the computationallogic as new parallel processors are developed. Instead, the machinelearning application can be configured to perform the necessarycomputations using the primitives provided by the machine learningframework 604. Exemplary primitives include tensor convolutions,activation functions, and pooling, which are computational operationsthat are performed while training a convolutional neural network (CNN).The machine learning framework 604 can also provide primitives toimplement basic linear algebra subprograms performed by manymachine-learning algorithms, such as matrix and vector operations.

The machine learning framework 604 can process input data received fromthe machine learning application 602 and generate the appropriate inputto a compute framework 606. The compute framework 606 can abstract theunderlying instructions provided to the GPGPU driver 608 to enable themachine learning framework 604 to take advantage of hardwareacceleration via the GPGPU hardware 610 without requiring the machinelearning framework 604 to have intimate knowledge of the architecture ofthe GPGPU hardware 610. Additionally, the compute framework 606 canenable hardware acceleration for the machine learning framework 604across a variety of types and generations of the GPGPU hardware 610.

GPGPU Machine Learning Acceleration

FIG. 7 illustrates a general-purpose graphics processing unit 700,according to an embodiment. In one embodiment, the general-purposeprocessing unit (GPGPU) 700 can be configured to be particularlyefficient in processing the type of computational workloads associatedwith training deep neural networks. Additionally, the GPGPU 700 can belinked directly to other instances of the GPGPU to create a multi-GPUcluster to improve training speed for particularly deep neural networks.

The GPGPU 700 includes a host interface 702 to enable a connection witha host processor. In one embodiment the host interface 702 is a PCIExpress interface. However, the host interface can also be a vendorspecific communications interface or communications fabric. The GPGPU700 receives commands from the host processor and uses a globalscheduler 704 to distribute execution threads associated with thosecommands to a set of compute clusters 706A-706H. The compute clusters706A-706H share a cache memory 708. The cache memory 708 can serve as ahigher-level cache for cache memories within the compute clusters706A-706H.

The GPGPU 700 includes memory 714A-714B coupled with the computeclusters 706A-706H via a set of memory controllers 712A-712B. In variousembodiments, the memory 714A-714B can include various types of memorydevices including dynamic random-access memory (DRAM) or graphicsrandom-access memory, such as synchronous graphics random access memory(SGRAM), including graphics double data rate (GDDR) memory. In oneembodiment, the memory 714A-714N may also include 3D stacked memory,including but not limited to high bandwidth memory (HBM).

In one embodiment, each of the compute clusters 706A-706H includes a setof graphics multiprocessors, such as the graphics multiprocessor 400 ofFIG. 4A. The graphics multiprocessors of the compute cluster multipletypes of integer and floating-point logic units that can performcomputational operations at a range of precisions including suited formachine learning computations. For example, and in one embodiment atleast a subset of the floating-point units in each of the computeclusters 706A-H can be configured to perform 16-bit or 32-bit floatingpoint operations, while a different subset of the floating-point unitscan be configured to perform 64-bit floating point operations.

Multiple instances of the GPGPU 700 can be configured to operate as acompute cluster. The communication mechanism used by the compute clusterfor synchronization and data exchange varies across embodiments. In oneembodiment, the multiple instances of the GPGPU 700 communicate over thehost interface 702. In one embodiment the GPGPU 700 includes an I/O hub709 that couples the GPGPU 700 with a GPU link 710 that enables a directconnection to other instances of the GPGPU. In one embodiment the GPUlink 710 is coupled to a dedicated GPU-to-GPU bridge that enablescommunication and synchronization between multiple instances of theGPGPU 700. In one embodiment the GPU link 710 couples with a high-speedinterconnect to transmit and receive data to other GPGPUs or parallelprocessors. In one embodiment the multiple instances of the GPGPU 700are located in separate data processing systems and communicate via anetwork device that is accessible via the host interface 702. In oneembodiment the GPU link 710 can be configured to enable a connection toa host processor in addition to or as an alternative to the hostinterface 702.

While the illustrated configuration of the GPGPU 700 can be configuredto train neural networks, one embodiment provides alternateconfiguration of the GPGPU 700 that can be configured for deploymentwithin a high performance or low power inferencing platform. In aninferencing configuration, the GPGPU 700 includes fewer of the computeclusters 706A-706H relative to the training configuration. Additionally,memory technology associated with the memory 714A-714B may differbetween inferencing and training configurations. In one embodiment, theinferencing configuration of the GPGPU 700 can support inferencingspecific instructions. For example, an inferencing configuration canprovide support for one or more 8-bit integer dot product instructions,which are commonly used during inferencing operations for deployedneural networks.

FIG. 8 illustrates a multi-GPU computing system 800, according to anembodiment. The multi-GPU computing system 800 can include a processor802 coupled to multiple GPGPUs 806A-D via a host interface switch 804.The host interface switch 804, in one embodiment, is a PCI expressswitch device that couples the processor 802 to a PCI express bus overwhich the processor 802 can communicate with the set of GPGPUs806A-806D. Each of the multiple GPGPUs 806A-806D can be an instance ofthe GPGPU 700 of FIG. 7. The GPGPUs 806A-806D can interconnect via a setof high-speed point-to-point GPU to GPU links 816. The high-speed GPU toGPU links can connect to each of the GPGPUs 806A-806D via a dedicatedGPU link, such as the GPU link 710 as in FIG. 7. The P2P GPU links 816enable direct communication between each of the GPGPUs 806A-806D withoutrequiring communication over the host interface bus to which theprocessor 802 is connected. With GPU-to-GPU traffic directed to the P2PGPU links, the host interface bus remains available for system memoryaccess or to communicate with other instances of the multi-GPU computingsystem 800, for example, via one or more network devices. While in theillustrated embodiment the GPGPUs 806A-806D connect to the processor 802via the host interface switch 804, in one embodiment the processor 802includes direct support for the P2P GPU links 816 and can connectdirectly to the GPGPUs 806A-806D.

Machine Learning Neural Network Implementations

The computing architecture provided by embodiments described herein canbe configured to perform the types of parallel processing that isparticularly suited for training and deploying neural networks formachine learning. A neural network can be generalized as a network offunctions having a graph relationship. As is well-known in the art,there are a variety of types of neural network implementations used inmachine learning. One exemplary type of neural network is thefeedforward network, as previously described.

A second exemplary type of neural network is the Convolutional NeuralNetwork (CNN). A CNN is a specialized feedforward neural network forprocessing data having a known, grid-like topology, such as image data.Accordingly, CNNs are commonly used for compute vision and imagerecognition applications, but they also may be used for other types ofpattern recognition such as speech and language processing. The nodes inthe CNN input layer are organized into a set of “filters” (featuredetectors inspired by the receptive fields found in the retina), and theoutput of each set of filters is propagated to nodes in successivelayers of the network. The computations for a CNN include applying theconvolution mathematical operation to each filter to produce the outputof that filter. Convolution is a specialized kind of mathematicaloperation performed by two functions to produce a third function that isa modified version of one of the two original functions. Inconvolutional network terminology, the first function to the convolutioncan be referred to as the input, while the second function can bereferred to as the convolution kernel. The output may be referred to asthe feature map. For example, the input to a convolution layer can be amultidimensional array of data that defines the various color componentsof an input image. The convolution kernel can be a multidimensionalarray of parameters, where the parameters are adapted by the trainingprocess for the neural network.

Recurrent neural networks (RNNs) are a family of feedforward neuralnetworks that include feedback connections between layers. RNNs enablemodeling of sequential data by sharing parameter data across differentparts of the neural network. The architecture for a RNN includes cycles.The cycles represent the influence of a present value of a variable onits own value at a future time, as at least a portion of the output datafrom the RNN is used as feedback for processing subsequent input in asequence. This feature makes RNNs particularly useful for languageprocessing due to the variable nature in which language data can becomposed.

The figures described below present exemplary feedforward, CNN, and RNNnetworks, as well as describe a general process for respectivelytraining and deploying each of those types of networks. It will beunderstood that these descriptions are exemplary and non-limiting as toany specific embodiment described herein and the concepts illustratedcan be applied generally to deep neural networks and machine learningtechniques in general.

The exemplary neural networks described above can be used to performdeep learning. Deep learning is machine learning using deep neuralnetworks. The deep neural networks used in deep learning are artificialneural networks composed of multiple hidden layers, as opposed toshallow neural networks that include only a single hidden layer. Deeperneural networks are generally more computationally intensive to train.However, the additional hidden layers of the network enable multisteppattern recognition that results in reduced output error relative toshallow machine learning techniques.

Deep neural networks used in deep learning typically include a front-endnetwork to perform feature recognition coupled to a back-end networkwhich represents a mathematical model that can perform operations (e.g.,object classification, speech recognition, etc.) based on the featurerepresentation provided to the model. Deep learning enables machinelearning to be performed without requiring hand crafted featureengineering to be performed for the model. Instead, deep neural networkscan learn features based on statistical structure or correlation withinthe input data. The learned features can be provided to a mathematicalmodel that can map detected features to an output. The mathematicalmodel used by the network is generally specialized for the specific taskto be performed, and different models will be used to perform differenttask.

Once the neural network is structured, a learning model can be appliedto the network to train the network to perform specific tasks. Thelearning model describes how to adjust the weights within the model toreduce the output error of the network. Backpropagation of errors is acommon method used to train neural networks. An input vector ispresented to the network for processing. The output of the network iscompared to the desired output using a loss function and an error valueis calculated for each of the neurons in the output layer. The errorvalues are then propagated backwards until each neuron has an associatederror value which roughly represents its contribution to the originaloutput. The network can then learn from those errors using an algorithm,such as the stochastic gradient descent algorithm, to update the weightsof the of the neural network.

FIG. 9A-9B illustrate an exemplary convolutional neural network. FIG. 9Aillustrates various layers within a CNN. As shown in FIG. 9A, anexemplary CNN used to model image processing can receive input 902describing the red, green, and blue (RGB) components of an input image.The input 902 can be processed by multiple convolutional layers (e.g.,convolutional layer 904, convolutional layer 906). The output from themultiple convolutional layers may optionally be processed by a set offully connected layers 908. Neurons in a fully connected layer have fullconnections to all activations in the previous layer, as previouslydescribed for a feedforward network. The output from the fully connectedlayers 908 can be used to generate an output result from the network.The activations within the fully connected layers 908 can be computedusing matrix multiplication instead of convolution. Not all CNNimplementations make use of fully connected layers 908. For example, insome implementations the convolutional layer 906 can generate output forthe CNN.

The convolutional layers are sparsely connected, which differs fromtraditional neural network configuration found in the fully connectedlayers 908. Traditional neural network layers are fully connected, suchthat every output unit interacts with every input unit. However, theconvolutional layers are sparsely connected because the output of theconvolution of a field is input (instead of the respective state valueof each of the nodes in the field) to the nodes of the subsequent layer,as illustrated. The kernels associated with the convolutional layersperform convolution operations, the output of which is sent to the nextlayer. The dimensionality reduction performed within the convolutionallayers is one aspect that enables the CNN to scale to process largeimages.

FIG. 9B illustrates exemplary computation stages within a convolutionallayer of a CNN. Input to a convolutional layer 912 of a CNN can beprocessed in three stages of a convolutional layer 914. The three stagescan include a convolution stage 916, a detector stage 918, and a poolingstage 920. The convolution layer 914 can then output data to asuccessive convolutional layer. The final convolutional layer of thenetwork can generate output feature map data or provide input to a fullyconnected layer, for example, to generate a classification value for theinput to the CNN.

In the convolution stage 916 performs several convolutions in parallelto produce a set of linear activations. The convolution stage 916 caninclude an affine transformation, which is any transformation that canbe specified as a linear transformation plus a translation. Affinetransformations include rotations, translations, scaling, andcombinations of these transformations. The convolution stage computesthe output of functions (e.g., neurons) that are connected to specificregions in the input, which can be determined as the local regionassociated with the neuron. The neurons compute a dot product betweenthe weights of the neurons and the region in the local input to whichthe neurons are connected. The output from the convolution stage 916defines a set of linear activations that are processed by successivestages of the convolutional layer 914.

The linear activations can be processed by a detector stage 918. In thedetector stage 918, each linear activation is processed by a non-linearactivation function. The non-linear activation function increases thenonlinear properties of the overall network without affecting thereceptive fields of the convolution layer. Several types of non-linearactivation functions may be used. One particular type is the rectifiedlinear unit (ReLU), which uses an activation function defined asƒ(x)=max (0, x), such that the activation is thresholded at zero.

The pooling stage 920 uses a pooling function that replaces the outputof the convolutional layer 906 with a summary statistic of the nearbyoutputs. The pooling function can be used to introduce translationinvariance into the neural network, such that small translations to theinput do not change the pooled outputs. Invariance to local translationcan be useful in scenarios where the presence of a feature in the inputdata is more important than the precise location of the feature. Varioustypes of pooling functions can be used during the pooling stage 920,including max pooling, average pooling, and 12-norm pooling.Additionally, some CNN implementations do not include a pooling stage.Instead, such implementations substitute and additional convolutionstage having an increased stride relative to previous convolutionstages.

The output from the convolutional layer 914 can then be processed by thenext layer 922. The next layer 922 can be an additional convolutionallayer or one of the fully connected layers 908. For example, the firstconvolutional layer 904 of FIG. 9A can output to the secondconvolutional layer 906, while the second convolutional layer can outputto a first layer of the fully connected layers 908.

FIG. 10 illustrates an exemplary recurrent neural network 1000. In arecurrent neural network (RNN), the previous state of the networkinfluences the output of the current state of the network. RNNs can bebuilt in a variety of ways using a variety of functions. The use of RNNsgenerally revolves around using mathematical models to predict thefuture based on a prior sequence of inputs. For example, an RNN may beused to perform statistical language modeling to predict an upcomingword given a previous sequence of words. The illustrated RNN 1000 can bedescribed has having an input layer 1002 that receives an input vector,hidden layers 1004 to implement a recurrent function, a feedbackmechanism 1005 to enable a ‘memory’ of previous states, and an outputlayer 1006 to output a result. The RNN 1000 operates based ontime-steps. The state of the RNN at a given time step is influencedbased on the previous time step via the feedback mechanism 1005. For agiven time step, the state of the hidden layers 1004 is defined by theprevious state and the input at the current time step. An initial input(x₁) at a first time step can be processed by the hidden layer 1004. Asecond input (x₂) can be processed by the hidden layer 1004 using stateinformation that is determined during the processing of the initialinput (x₁). A given state can be computed as s_(t)=ƒ(Ux_(t)+Ws_(t-1)),where U and W are parameter matrices. The function ƒ is generally anonlinearity, such as the hyperbolic tangent function (Tanh) or avariant of the rectifier function ƒ(x)=max(0, x). However, the specificmathematical function used in the hidden layers 1004 can vary dependingon the specific implementation details of the RNN 1000.

In addition to the basic CNN and RNN networks described, variations onthose networks may be enabled. One example RNN variant is the longshort-term memory (LSTM) RNN. LSTM RNNs are capable of learninglong-term dependencies that may be necessary for processing longersequences of language. A variant on the CNN is a convolutional deepbelief network, which has a structure similar to a CNN and is trained ina manner similar to a deep belief network. A deep belief network (DBN)is a generative neural network that is composed of multiple layers ofstochastic (random) variables. DBNs can be trained layer-by-layer usinggreedy unsupervised learning. The learned weights of the DBN can then beused to provide pre-train neural networks by determining an optimalinitial set of weights for the neural network.

FIG. 11 illustrates training and deployment of a deep neural network.Once a given network has been structured for a task the neural networkis trained using a training dataset 1102. Various training frameworks1104 have been developed to enable hardware acceleration of the trainingprocess. For example, the machine learning framework 604 of FIG. 6 maybe configured as a training framework 604. The training framework 604can hook into an untrained neural network 1106 and enable the untrainedneural net to be trained using the parallel processing resourcesdescribed herein to generate a trained neural net 1108.

To start the training process the initial weights may be chosen randomlyor by pre-training using a deep belief network. The training cycle thenbe performed in either a supervised or unsupervised manner.

Supervised learning is a learning method in which training is performedas a mediated operation, such as when the training dataset 1102 includesinput paired with the desired output for the input, or where thetraining dataset includes input having known output and the output ofthe neural network is manually graded. The network processes the inputsand compares the resulting outputs against a set of expected or desiredoutputs. Errors are then propagated back through the system. Thetraining framework 1104 can adjust to adjust the weights that controlthe untrained neural network 1106. The training framework 1104 canprovide tools to monitor how well the untrained neural network 1106 isconverging towards a model suitable to generating correct answers basedon known input data. The training process occurs repeatedly as theweights of the network are adjusted to refine the output generated bythe neural network. The training process can continue until the neuralnetwork reaches a statistically desired accuracy associated with atrained neural net 1108. The trained neural network 1108 can then bedeployed to implement any number of machine learning operations togenerate an inference result 1114 based on input of new data 1112.

Unsupervised learning is a learning method in which the network attemptsto train itself using unlabeled data. Thus, for unsupervised learningthe training dataset 1102 will include input data without any associatedoutput data. The untrained neural network 1106 can learn groupingswithin the unlabeled input and can determine how individual inputs arerelated to the overall dataset. Unsupervised training can be used togenerate a self-organizing map, which is a type of trained neuralnetwork 1108 capable of performing operations useful in reducing thedimensionality of data. Unsupervised training can also be used toperform anomaly detection, which allows the identification of datapoints in an input dataset that deviate from the normal patterns of thedata.

Variations on supervised and unsupervised training may also be employed.Semi-supervised learning is a technique in which in the training dataset1102 includes a mix of labeled and unlabeled data of the samedistribution. Incremental learning is a variant of supervised learningin which input data is continuously used to further train the model.Incremental learning enables the trained neural network 1108 to adapt tothe new data 1112 without forgetting the knowledge instilled within thenetwork during initial training.

Whether supervised or unsupervised, the training process forparticularly deep neural networks may be too computationally intensivefor a single compute node. Instead of using a single compute node, adistributed network of computational nodes can be used to accelerate thetraining process.

FIG. 12 is a block diagram illustrating distributed learning.Distributed learning is a training model that uses multiple distributedcomputing nodes to perform supervised or unsupervised training of aneural network. The distributed computational nodes can each include oneor more host processors and one or more of the general-purposeprocessing nodes, such as the highly-parallel general-purpose graphicsprocessing unit 700 as in FIG. 700. As illustrated, distributed learningcan be performed model parallelism 1202, data parallelism 1204, or acombination of model and data parallelism 1204.

In model parallelism 1202, different computational nodes in adistributed system can perform training computations for different partsof a single network. For example, each layer of a neural network can betrained by a different processing node of the distributed system. Thebenefits of model parallelism include the ability to scale toparticularly large models. Splitting the computations associated withdifferent layers of the neural network enables the training of verylarge neural networks in which the weights of all layers would not fitinto the memory of a single computational node. In some instances, modelparallelism can be particularly useful in performing unsupervisedtraining of large neural networks. In another example of modelparallelism, computation in one or more layers of a neural network modelcan be split across multiple compute nodes across feature map dimensionto reduce size of per node model parameters.

In data parallelism 1204, the different nodes of the distributed networkhave a complete instance of the model and each node receives a differentportion of the data. The results from the different nodes are thencombined. While different approaches to data parallelism are possible,data parallel training approaches all require a technique of combiningresults and synchronizing the model parameters between each node.Exemplary approaches to combining data include parameter averaging andupdate based data parallelism. Parameter averaging trains each node on asubset of the training data and sets the global parameters (e.g.,weights, biases) to the average of the parameters from each node.Parameter averaging uses a central parameter server that maintains theparameter data. Update based data parallelism is similar to parameteraveraging except that instead of transferring parameters from the nodesto the parameter server, the updates to the model are transferred.Additionally, update-based data parallelism can be performed in adecentralized manner, where the updates are compressed and transferredbetween nodes.

Combined model and data parallelism 1206 can be implemented, forexample, in a distributed system in which each computational nodeincludes multiple GPUs. Each node can have a complete instance of themodel with separate GPUs within each node are used to train differentportions of the model.

Distributed training has increased overhead relative to training on asingle machine. However, the parallel processors and GPGPUs describedherein can each implement various techniques to reduce the overhead ofdistributed training, including techniques to enable high bandwidthGPU-to-GPU data transfer and accelerated remote data synchronization.

Exemplary Machine Learning Applications

Machine learning can be applied to solve a variety of technologicalproblems, including but not limited to computer vision, autonomousdriving and navigation, speech recognition, and language processing.Computer vision has traditionally been one of the most active researchareas for machine learning applications. Applications of computer visionrange from reproducing human visual abilities, such as recognizingfaces, to creating new categories of visual abilities. For example,computer vision applications can be configured to recognize sound wavesfrom the vibrations induced in objects visible in a video. Parallelprocessor accelerated machine learning enables computer visionapplications to be trained using significantly larger training datasetthan previously feasible and enables inferencing systems to be deployedusing low power parallel processors.

Parallel processor accelerated machine learning has autonomous drivingapplications including lane and road sign recognition, obstacleavoidance, navigation, and driving control. Accelerated machine learningtechniques can be used to train driving models based on datasets thatdefine the appropriate responses to specific training input. Theparallel processors described herein can enable rapid training of theincreasingly complex neural networks used for autonomous drivingsolutions and enables the deployment of low power inferencing processorsin a mobile platform suitable for integration into autonomous vehicles.

Parallel processor accelerated deep neural networks have enabled machinelearning approaches to automatic speech recognition (ASR). ASR includesthe creation of a function that computes the most probable linguisticsequence given an input acoustic sequence. Accelerated machine learningusing deep neural networks have enabled the replacement of the hiddenMarkov models (HMMs) and Gaussian mixture models (GMMs) previously usedfor ASR.

Parallel processor accelerated machine learning can also be used toaccelerate natural language processing. Automatic learning procedurescan make use of statistical inference algorithms to produce models thatare robust to erroneous or unfamiliar input. Exemplary natural languageprocessor applications include automatic machine translation betweenhuman languages.

The parallel processing platforms used for machine learning can bedivided into training platforms and deployment platforms. Trainingplatforms are generally highly parallel and include optimizations toaccelerate multi-GPU single node training and multi-node, multi-GPUtraining. Exemplary parallel processors suited for training include thehighly-parallel general-purpose graphics processing unit 700 of FIG. 700and the multi-GPU computing system 800 of FIG. 800. On the contrary,deployed machine learning platforms generally include lower powerparallel processors suitable for use in products such as cameras,autonomous robots, and autonomous vehicles.

FIG. 13 illustrates an exemplary inferencing system on a chip (SOC) 1300suitable for performing inferencing using a trained model. The SOC 1300can integrate processing components including a media processor 1302, avision processor 1304, a GPGPU 1306 and a multi-core processor 1308. TheSOC 1300 can additionally include on-chip memory 1305 that can enable ashared on-chip data pool that is accessible by each of the processingcomponents. The processing components can be optimized for low poweroperation to enable deployment to a variety of machine learningplatforms, including autonomous vehicles and autonomous robots. Forexample, one implementation of the SOC 1300 can be used as a portion ofthe main control system for an autonomous vehicle. Where the SOC 1300 isconfigured for use in autonomous vehicles the SOC is designed andconfigured for compliance with the relevant functional safety standardsof the deployment jurisdiction.

During operation, the media processor 1302 and vision processor 1304 canwork in concert to accelerate computer vision operations. The mediaprocessor 1302 can enable low latency decode of multiple high-resolution(e.g., 4K, 8K) video streams. The decoded video streams can be writtento a buffer in the on-chip-memory 1305. The vision processor 1304 canthen parse the decoded video and perform preliminary processingoperations on the frames of the decoded video in preparation ofprocessing the frames using a trained image recognition model. Forexample, the vision processor 1304 can accelerate convolution operationsfor a CNN that is used to perform image recognition on thehigh-resolution video data, while back end model computations areperformed by the GPGPU 1306.

The multi-core processor 1308 can include control logic to assist withsequencing and synchronization of data transfers and shared memoryoperations performed by the media processor 1302 and the visionprocessor 1304. The multi-core processor 1308 can also function as anapplication processor to execute software applications that can make useof the inferencing compute capability of the GPGPU 1306. For example, atleast a portion of the navigation and driving logic can be implementedin software executing on the multi-core processor 1308. Such softwarecan directly issue computational workloads to the GPGPU 1306 or thecomputational workloads can be issued to the multi-core processor 1308,which can offload at least a portion of those operations to the GPGPU1306.

The GPGPU 1306 can include compute clusters such as a low powerconfiguration of the compute clusters 706A-706H within thehighly-parallel general-purpose graphics processing unit 700. Thecompute clusters within the GPGPU 1306 can support instruction that arespecifically optimized to perform inferencing computations on a trainedneural network. For example, the GPGPU 1306 can support instructions toperform low precision computations such as 8-bit and 4-bit integervector operations.

Quantization Framework for Deep Learning Applications

A method and apparatus are described to perform quantization and datarepresentation of low-precision tensors in deep learning applications.Each low-precision tensor may contain a data buffer and associatedmetadata represented as a data structure. The metadata may containinformation pertaining to data type (integer, fixed-point, float or anyother custom data type), precision and shared exponent(s)/scalingfactor(s) necessary for performing data conversions and arithmeticoperations. The data buffer may be stored as one contiguous block ormany smaller blocks with as many exponents/scaling factors correspondingto each block. Smaller blocks with fine-grained scaling may be used tocapture wider dynamic range (typically required for the gradients duringtraining) with each block scaled independently to fit into the range andprecision of the low-precision data type.

Embodiments described herein provide for a dynamic fixed-pointrepresentation that can be used to store quantized floating-point data.A method and apparatus are described to perform dynamic fixed-pointquantization (DFPQ) to convert between floating-point numbers tointegers with shared exponent. A dynamic fixed-point data representationis also described, along with associated formulas for performing basicarithmetic operations. In one embodiment, a 16-bit dynamic fixed-pointrepresentation is used that has a 1-bit sign, 15-bits of magnitude, anduses a separately stored 8-bit shared exponent. However, the size of theshared exponent can vary in different fixed-point representations. Forexample, 5-bit, 8-bit, 16-bit, or 32-bit shared exponents can be used.In one embodiment, any size of signed integer can be used as a sharedexponent and data can be represented by any sized signed or unsignedintegers. To convert from floating-point to traditional fixed-point, onecan multiply the floating-point value by 2^(fb), where fb is the numberof fractional bits for the target fixed-point representation (e.g., 2⁸,for 24.8 fixed-point) and round the result to the nearest integer.Alternatively, some implementations use lookup tables or hash tables toperform this conversion. However, these techniques can result insignificant data loss, depending on the dynamic range of the data to beconverted. Additionally, traditional systems generally use a singlefixed-point format for the data set, instead of dynamically varying therepresentation per block of data (e.g., tensor, neural network layer,etc.). In some embodiments data can be processed in blocks, with eachblock having an associated shared exponent. While blocked dynamicfixed-point representations are described herein, the blocked datarepresentation can also be used for any other standard or custom lowprecision fixed or floating-point data.

FIG. 14 illustrates a comparison between floating-point and a dynamicfixed-point format, according to an embodiment. Quantization issupported for multiple floating-point formats to multiple dynamicfixed-point bit-widths. Single precision (e.g., 32-bit) floating-point1410 to 16-bit dynamic fixed-point 1420 are illustrated for exemplarypurposes. The IEEE-754 single-precision encoding is illustrated in whicha floating-point value includes a sign bit 1411, an 8-bit exponent 1412,and 23-bits of explicit mantissa 1413. An 8×8 tensor 1415 is illustratedin which 64 floating-point values are stored at 32-bits per entry.

The floating-point representation 1410 can be quantized into a 16-bitdynamic fixed-point representation 1420 with minimal data loss. Thedynamic fixed-point representation enables an 8×8 tensor 1415 of 32-bitfloating-point values 1414 to be stored in an 8×8 tensor 1425 of 16-bitinteger values, each associated with an 8-bit shared exponent. Thefloating-point sign bit 1411 is preserved as the fixed-point sign bit1421. 32-bits of exponent 1412 and mantissa 1413 (including the implicitleading bit) can be stored using a 15-bit magnitude 1422 and a sharedexponent 1423, which is used as a scaling factor for the entire 8×8tensor 1425.

FIG. 15A illustrates dynamic fixed-point quantization, according to anembodiment. Dynamic fixed-point quantization is performed based on theabsolute max values within the tensor to be quantized. The illustratedquantization is performed as indicated by equation (1).

I _(x) ={S _(x) ,b _(Clip) _(N-1) (M _(X)>>((E _(abs_max) −E_(x))+1)},P=16  (1)

The quantized value is associated with a shared scale factor (SF) whichacts as a shared exponent for the tensor. SF is calculated as shown inequation (2).

SF={E _(abs_max)−127}  (2)

Equation (1) and equation (2) may be applied to quantize a value withina tensor, where the tensor has an absolute max value 1502 of 318.00((ƒ_(abs_max)=318.00). The absolute max value 1502 is associated with anabsolute max exponent 1504A and an absolute max mantissa 1504B, with theexemplary E_(abs_max)=135. To quantize an exemplary floating-point value1512 (ƒ_(x)=3.4667968) having an exponent 1514A (E_(x)) and a mantissa1514B (M_(x)), the mantissa 1514B is right shifted by the differencebetween the exponent 1514A and the absolute max value exponent to createa magnitude integer 1524 (I_(x)), with the implicit leading bit 1513(LB) stored as an explicit bit 1523 within the magnitude integer 1524.The sign bit 1520 (S_(x)) is maintained for the quantized fixed-pointvalue. The scaled exponent scale factor 1522 (SF) is computed as shownin equation (2) above.

FIG. 15B illustrates de-quantization, according to an embodiment. In oneembodiment de-quantization is the reverse of quantization. The commonscale factor 1522 can be used to convert each magnitude integer 1524(I_(x)) into a floating-point value after a round of fixed-pointcomputations. The magnitude integer 1524 (I_(x)) is converted by passingthe value into a leading zero detector logic 1530 (LZD) to generate anLZ_(x) value 1532 that indicates the number of leading zeros in themagnitude integer 1524. The magnitude integer 1524 (I_(x)) is leftshifted by shift logic based on the LZ_(x) value 1532 and the explicitlystored leading bit is restored to an implicit leading bit 1533. As withquantization, the sign bit 1520 is unchanged. The de-quantized exponent1534A (E_(x)) is computed as shown in equation (3).

E _(x)={127+SF+LZ _(x)+1}  (3)

The floating-point value can then be constructed based on thede-quantized sign, exponent, and mantissa.

A generic dynamic fixed-point formulation is shown in the equations tofollow. To convert a 32-bit floating-point tensor f_(tensor) intodfP_(tensor), where f_(tensor)={f₀, f₁, f₂, . . . f_(n),}, a sharedblock exponent (e.g., scale factor SF) is computed via equation (4).

SF=SH _(exp) =E(maxabs(f _(tensor))−127  (4)

Furthermore, dfp_(tensor)={I_(tensor), SF}, where I_(tensor)={I₀, I₁,I₂, . . . I_(n),}. Elements of I_(tensor) are computed as in equation(5).

I _(x)=└ƒ_(x)×2^((P-SF-2))┘  (5)

In equation (5), P is the number of bits used to represent I_(x). Forexample, for a 16-bit dynamic fixed-point value (dfp¹⁶), P=16.

FIG. 16A illustrates equations for arithmetic operations (addition,multiplication) and down conversion to support input and output of a16-bit dynamic fixed-point format. The equations are defined where dfp₁¹⁶=<I₁ ¹⁶,SF₁> and dfp₂ ¹⁶=<I₂ ¹⁶, SF₂> are values from two different16-bit dfp tensors, then addition 1610, multiplication 1620, and downconversion 1630 can be defined as illustrated. The addition 1610arithmetic operation is defined as:

dfp ₁ ¹⁶ +dfp ₂ ¹⁶ =<I _(sum) ¹⁶,max(SF₁,SF₂)), where

-   -   I_(sum) ¹⁶=(I₁ ¹⁶>>(max(SF₁,SF₂)−SF₁))+(I₂        ¹⁶>>(max(SF₁,SF₂)−SF₂))

The multiplication 1620 arithmetic operation is defined as:

dfP _(prod) ³² =dfp ₁ ×dfp ₂ =<I _(prod) ³²,(SF₁+SF₂)), where

-   -   I_(prod) ³²=I₁ ¹⁶*I₂ ¹⁶ is a 32-bit integer

The down conversion 1630 arithmetic operation (dft³²→dft¹⁶) is definedas:

dfp _(dc) ¹⁶ =<I _(dc) ¹⁶,(sf³² −R _(shift))>, where

-   -   I_(dc) ¹⁶=(I³²>>R_(shift)) and R_(shift)=(16−LZC(I³²)−1)

In the above equation, LZC refers to a leading zero count operation.

Embodiments described herein are not limited to computations using16-bit dynamic fixed-point data types. One embodiment provides forcomputational logic having support for hardware accelerated primitivesto perform conversion between floating-point and dynamic fixed-pointdata of varying sizes. Such embodiment can also provide support forhardware accelerated primitives to perform arithmetic operations ondynamic fixed-point data.

When converting floating-point tensors to dynamic fixed-point, theshared exponent is derived from the exponent of absolute maximum valueof the floating-point tensor. If F if a floating-point tensor, theexponent is the absolute maximum value as expressed by equation (6).

$\begin{matrix}{E_{f\max} = {E\left( {\begin{matrix}\max \\{\forall{f \in F}}\end{matrix}{f}} \right)}} & (6)\end{matrix}$

The value of the shared exponent E_(S) is a function of E_(ƒmax) and thenumber of bits P used by the output integer tensor I, as shown inequation (7).

E _(s) =E _(ƒmax)−(P−2)  (7)

The relationship of the resulting DFP tensor<I−E_(s)> with the inputfloating-point tensor F is expressed as in equation (8).

∀i _(n) ϵI,ƒ _(n) =i _(n)×2^(E) ^(s) , where ƒ_(n) ϵF  (8)

Based on the basic formulation above, a set of dynamic fixed-pointprimitives for use in neural network training can be defined as shownbelow, and as illustrated in FIG. 16B.

FIG. 16B illustrates equations for hardware accelerated dynamicfixed-point primitives, according to an embodiment. Operations for anaddition 1640 primitive can be defined as:

$i_{a + b} = \left\{ {{\begin{matrix}{{i_{a} + \left( {i_{b}\left( {E_{s}^{a} - E_{s}^{b}} \right)} \right)}\ ,\ {{{when}\ E_{s}^{a}} > E_{s}^{b}}} \\{{i_{b} + \left( {i_{a}\left( {E_{s}^{b} - E_{s}^{a}} \right)} \right)},{{{when}\mspace{14mu} E_{s}^{b}} > E_{s}^{a}}}\end{matrix}\mspace{14mu} {and}\mspace{14mu} {exponent}},\ {E_{s}^{a + b} = \begin{matrix}\max \\{E_{s}^{a},E_{s}^{b}}\end{matrix}}} \right.$

Operations for a multiplication primitive 1650 can be defined as:

i _(ab) =i _(a) ×i _(b) and exponent,E _(s) ^(ab) =E _(s) ^(a) +E _(s)^(b)

In one embodiment, when a fused multiply and add operation is performed,all products can have the same shared exponent: E_(s) ^(ab)=E_(s)^(a)+E_(s) ^(b), hence the sum of such products has the same sharedexponent.

Down conversion can be performed to scale higher-precision dynamicfixed-point data output from one layer of a neural network to a lowerprecision for input to a subsequent layer. Data of an N-bit I tensor canbe right-shifted R_(s) bits to fit into a lower precision tensor.Operations for a down-conversion primitive 1660 can be defined as:

$R_{s} = {P - {{LZC}\mspace{11mu} \left( {\begin{matrix}\max \\{\forall{i_{ab} \in I^{N}}}\end{matrix}{i_{ab}}} \right)}}$l_(ab)^(d) = i_(ab)R_(s)  and  component, E_(s)^(ab)+ = R_(s)

While dynamic precision is described herein with respect to fixed-pointcomputations, dynamic precision operations can be also extended tofloating-point, low precision floating-point, and custom definedfloating-point data types. In particular, blocked dynamic precisionoperations, as described further in FIG. 21A-21D, can be appliedgenerally to low-precision data types to enable computations on datahaving a larger dynamic range that could otherwise be supported bylow-precision data types.

Biased Rounding for Training Low-Precision Integer Neural Networks

One embodiment provides a method and apparatus for biased rounding toimprove training accuracy using low-precision integer networks. Thebiased rounding technique described herein realizes performance equal toor better than stochastic rounding techniques in use in someimplementations of low-precision training. The biased rounding techniqueavoid the requirement of a random number generator for computing therandom numbers used in stochastic rounding, enabling reduced latency andpower consumption relative to stochastic rounding.

In one embodiment, biased rounding techniques are performed whenperforming a quantization from a 32-bit floating-point value to a 16-bitdynamic fixed-point value or when down-converting a 32-bit dynamicfixed-point value to a 16-bit dynamic fixed-point value. The biasedrounding techniques improve the likelihood that the quantized ordown-converted data set has a similar data distribution as thepre-quantized or pre-down-converted data

The biased rounding logic is configured to have a bias towards roundingup. In one embodiment this bias is enabled using an extra bit thatrepresents 0.25ε as the bias bit, where ε is the smallest value that canbe represented in the target precision. A truth table for the roundingdecision for a single bias bit is shown in Table 5.

TABLE 5 Biased Rounding Truth Table Round Bit Bias Bit Rounding 0 0 DOWN1 0 UP 0 1 UP 1 1 UP

The bias towards rounding up is shown in Table 5, where a round-up isperformed unless both the round bit and the bias bit are zero. The truthtable of Table 5 is exemplary of one embodiment in which a single roundbit and a single bias bit are present. In one embodiment, two bias bitsare present. In other embodiments, three or more bias bits are present.In such embodiments, the bias can be adjusted based on the input datadistribution.

FIG. 17 illustrates floating-point to dynamic fixed-point biasedrounding, according to an embodiment. Quantization with biased roundingas illustrated in FIG. 17 is similar to quantization as illustrated inFIG. 15A. Additionally, a round bit 1740 and a bias bit 1742 are used tocapture bits that would otherwise be lost during the right shift togenerate the integer magnitude value. Based on the round bit 1740 andbias bit 1742 valued, a truth function or truth table, such as the truthtable shown in Table 5, is applied based on the round and bias values. Arounded magnitude 1744 can then be generated that is rounded or notrounded based on the truth table.

Dynamic Precision Management for Integer Deep Learning Primitives

Overflow and/or saturation of accumulator during integer arithmeticoperations introduces significant computational errors while performinglonger accumulation chains (such as GEMM or Convolution). One embodimentenables techniques to dynamically adjust the effective precision of theinput using dynamic fixed-point representation to prevent such errorswith a minimum impact on accuracy.

FIG. 18 is a block diagram of a multiprocessor unit 1800, according toan embodiment. The multiprocessor unit 1800 can be a variant of agraphics multiprocessor 234 of FIG. 2D. The multiprocessor unit 1800includes a fetch and decode unit 1802, a branch unit 1804, a registerfile 1806, a thread manager 1808, a single instruction multiple threadunit (SIMT unit 1809), and a dynamic precision manager 1819. The fetchand decode unit 1802 can fetch an instruction for execution by themultiprocessor unit 1800. The instruction can cause one or more of thecompute units (e.g., compute unit 1810) of the SIMT unit 1809 to performcomputational operations associated with a neural network as describedherein. In one embodiment, the instruction causes a compute unit 1810 toperform a dynamic precision computation using a dynamic fixed-point datatype as described herein. The branch unit 1804 can compute instructionpointer adjustments based on an executed jump instruction. The registerfile 1806 can store general-purpose and architectural registers used bythe SIMT unit 1809. The thread manager 1808 can distribute andre-distribute threads among the compute units of the SIMT unit 1809. Inone embodiment, the SIMT unit 1809 is configured to execute a singleinstruction as multiple threads, with each thread of the instructionexecuted by a separate compute unit. In one embodiment compute unit 1811through compute unit 1818 each includes an integer ALU (e.g., ALU1811A-1818A) and a floating-point unit (e.g., FPU 1811B-1818B). Thevoltage and frequency of each compute unit 1811-1818 within the SIMTunit 1809 can be dynamically managed by a voltage and frequency manager,which can increase or decrease the voltage and clock frequency suppliedto the various compute units as components of the compute units areenabled and disabled.

In some previously enable configurations, each compute unit can executea single thread of either an integer instruction or a floating-pointinstruction. If any of ALU 1811A-1818A is tasked to execute a thread ofan integer instruction, respective FPU 1811B-FPU 1418B is unavailablefor use to execute a thread of a floating-point instruction and may bepower gated during the operation of the corresponding ALU 1811A-ALU1818A. For example, while ALU 1811A may execute a thread of an integerinstruction while FPU 1813B executes a thread of a floating-pointinstruction, FPU 1811B is power gated while ALU 1813A is active.Embodiments described herein overcome such limitations by enabling, forexample, ALU 1811A to execute thread of an instruction while FPU 1811Bexecutes a thread of a different instruction. Furthermore, oneembodiment provides support for mixed precision or mixed data-typeoperands, such that a single instruction can simultaneously performoperations for an instruction having floating-point and integer operandsand/or operands having different precisions.

Embodiments described herein enable increased operational throughput fora cluster of compute units by making all logic units within each computeunit available to perform computations. In such embodiments, logic unitswithin a compute unit that are designed to perform computationsselectively at one of multiple precisions or multiple data types can beconfigured to perform multiple simultaneous operations for eachprecision or data type supported by the compute unit. For a givencompute unit 1811-1818, ALUs 1811A-1818A can perform integer operationswhile FPU 1811B-1818B perform floating-point operations. Theseoperations can be performed for a single instruction or for multipleinstruction

In one embodiment, the dynamic precision manager 1819 can be configuredto dynamically adjust the precision of compute operations, for example,to prevent overflows from occurring during a chain of accumulations.Longer accumulation chains typically require larger accumulators. Thenumber of extra bits needed for the accumulator are ˜log₂ n, where n isthe number of accumulations. Some embodiments described herein can beconfigured to dynamically adjust the ‘effective precision’ of thecomputation. Such techniques can be applied on input data and/or theintermediate output of the ongoing accumulation chain. Intermediate datais shifted ‘right’ by R_(shift) before passing the intermedia datathrough next iteration of computation. The R_(shift) value is computedbased on heuristics and can be incremented or decremented to keep asmany precision bits as possible while keeping overflow in check.

FIG. 19A-19B illustrates logic units 1920, 1940 configured with dynamicprecision capability, according to embodiments. In one embodiment, thelogic units 1920, 1940 can reside within a compute unit 1810 as in FIG.18.

As shown in FIG. 19A, a logic unit 1920 can process input values 1921 by16-bit multiply-add logic 1922 that includes a 16 b×16 b multiplier 1924and a 32-bit adder 1926. The 32-bit adder 1926 can add a product 1925 ofa multiply operation with an accumulator value 1923 read from anaccumulator 1934, which in one embodiment is a 32-bit integer register.The 32-bit adder 1926 can output an integer sum 1928 that is in adynamic fixed-point representation. At various points within the logic,shifters (e.g., shift logic 1927, 1933, 1935) can be used to shift theresults to dynamically adjust precision and prevent overflow. A sharedexponent, or scale factor (SF), can be stored in an SF register 1930 toenable reversion of the precision preserving shifts to generate outputdata. The scale factor stored in the SF register 1930 can be determinedbased on a precision heuristics unit 1929, which can adjust the scalefactor to keep as many precision bits as possible without overflowing.The precision heuristics unit 1929, in one embodiment, can reside atleast in part in the dynamic precision manager 1819 of FIG. 18.

In one embodiment, metadata 1932 can also be maintained along with ablock of dynamic fixed-point data to track a shared scale factor for thedata. The metadata 1932 can be stored in line with a block of thedynamic fixed-point data or can be stored in a separate metadatastructure that tracks scale factors for multiple blocks of dynamicfixed-point data. In one embodiment, the metadata can be, or can bederived from, scale factor data that is stored in the scale factorregister 1930. In one embodiment the metadata for a block of dynamicfixed-point data can be used by shift logic 1933 to shift the dynamicfixed-point data before storing the data in an accumulator 1934. In oneembodiment, a leading bit detector 1931 can be used to detect leadingones or leading zeros in the integer sum 1928. In such embodiment, thenumber of leading zeros or leading ones can be used as a heuristic todetermine whether to shift intermediate output. In one embodiment, theleading bit detector 1931 can be configured to detect only leading zeroswhen an absolute value is to be examined.

While specific sizes of data types and logic units are illustrated inFIG. 19, embodiments are not limited to any specific size, as the sizeof the multiplier 1924, adder 1926, and other logic described herein canvary across embodiments. Additionally, dynamic precision logic describedherein is not limited to dynamic precision fixed-point (e.g., integer)logic and data types.

FIG. 19B illustrates dynamic precision computation using combineddynamic fixed-point and floating-point operations. A logic unit 1940similar to logic unit 1920 can replace shift logic 1927, 1933 withfloating-point logic elements capable of computations with a largerdynamic range. Logic unit 1940 can include an inner block 1942 includingan N-bit×N-bit integer multiplier 1944 that outputs an integer product1945 of a pre-determined bit length based on the instruction orprimitive to be executed, or parameters provided to the instruction orprimitive to be executed. An N-bit adder 1946 can add the product 1945with an accumulator value 1943 read from an accumulator 1950, which canbe an integer register of one of multiple pre-determined sizes (e.g.,8-bit, 16-bit, 32-bit, 40-bit, etc.). The inner block 1942 can beconfigured to execute a number of multiply and accumulate passes oninput data, where the number of passes is determined to be a number thatwill not overflow the bit length configured for the inner block 1942. Inone embodiment, the number of passes to be performed by the inner block1942 can be predetermined for an input 1921 by the dynamic precisionmanager 1819 as in FIG. 18 and configured via the precision heuristicsmodule 1929.

Once a pre-determined number of iterations has been performed by theinner block 1942, the data can be converted from dynamic fixed-point tofloating-point via a dynamic fixed-point to floating-point converterunit 1954. The conversion can be performed based on a pre-scale factorstored as part of the metadata 1932 for the output tensor. The convertedoutput can then be accumulated to a floating-point accumulator register1956, which in one embodiment can be a 32-bit (FP32) register. In oneembodiment, additional operations for the tensor data can be performedin floating-point. Logic 1940 can be used to enable training for adataset to be performed at least in part in a dynamic fixed-pointprecision, enabling a performance and efficiency gain during the earlierportion of training, reducing the overall training time for a neuralnetwork.

FIG. 20 is a flow diagram illustrating logic 2000 to enable dynamicprecision management for integer deep learning primitives, according toan embodiment. The logic 2000 can receive a set of dynamic fixed-pointtensors as input, as shown at block 2002. The logic 2000 can thendetermine whether the shift the inputs, as shown at block 2003. If theinputs are to be shifted, the logic 2000 can compute an ‘rshift’(right-shift) value using the absolute max value and dynamic range ofthe tensors at block 2012. The logic 2000 can then right-shift the databased on rshift value and increment the shared exponent, as shown atblock 2014. If a shift is not required, or after the shift is performed,the logic 2000 can then perform the necessary compute operations (e.g.,MUL, ADD, MADD) at block 2004.

After the compute operation is performed at block 2004, the logic 2000can then check the leading zero count of the absolute max of the outputtensor at block 2006. If the leading zero count is above the overflowthreshold at block 2007, then the logic 2000 can adjust the precision ofthe output tensor based on the leading zero count. Adjusting theprecision of the output tensor includes adjusting the rshift count theshared exponent for the output. For example, the logic 2000 can increasethe rshift count for the output and increment the shared exponent if theleading count zero is above the overflow threshold at block 2016.Otherwise the logic 2000 can decrease the rshift count for the outputand decrement the shared exponent at block 2008. Adjusting the rshiftcount can include adjusting an rshift counter configured to track adegree of right-shift applied to the output tensor. Adjusting the rightshift count can correspond with applying a right-shift to the outputdata. After the increasing or decreasing the rshift counter andincrementing or decrementing the shared exponent, additional computeoperations can be performed by returning to block 2004.

FIG. 21A-21D illustrate blocked dynamic multi-precision data operations,according to embodiments described herein. Blocked dynamicmulti-precision data operations can be performed for dynamic fixed-pointdata and can also be generalized to enable block level scaling for anylow precision data type. In a training scenario, some tensors can beblocked, while other tensors can be non-blocked. For example, backpropagation may require a larger dynamic range, so the computationallogic can be configured to block the tensor data using smaller blocksizes. For forward propagation computations, blocking may not berequired.

In one embodiment, variable block sizes can be used, with the blocksizes and scale factors maintained in metadata for the data block. Whenprocessing blocked, multi-precision tensor data, computational logic canread the metadata for a block and configure the computational units toperform logic operations on the data block.

FIG. 21A illustrates a memory map 2100 associated with blocked dynamicfixed-point, according to an embodiment. FIG. 21B illustrates a blockedmulti-dimensional fine-grained quantized tensor 2110, according to anembodiment. FIG. 21C shows an exemplary group 2120 of 3×3 filters thatcan be represented in a blocked dynamic-precision data type. FIG. 21Dillustrates a process 2130 of performing blocked dynamic fixed-pointoperations, according to an embodiment.

A higher dynamic range requires more precision bits to represent thefull range using integers. One solution is to split the tensor intosmaller blocks with independent shared exponent while maintaining theinteger data at lower precision. This technique is useful for expressinglarge matrix multiplication and convolution operations. Using smallerblocks enables higher effective precision within each block and helpsachieve better overall accuracy using low-precision computation.

As illustrated in FIG. 21A, a 64×64 tensor 2100 can be divided into four32×32 blocks 2102, 2103, 2106, 2107. Each of the 32×32 blocks can have aseparate shared exponent/scaling factor in metadata 2101, 2104, 2105,2108 associated with each block. The metadata 2101, 2104, 2105, 2108 canbe stored contiguously with each of the 32×32 blocks 2102, 2103, 2106,2107, or can be stored in a separate region of memory. In addition toshared exponent or scaling factor data, the metadata can also containother terms used for data conversions, such as floating-point to fixedpoint or fixed-point to floating-point conversions. Additionally,blocking can be performed for tensors data stored in low-precision orcustom floating-point formats, with the data being shifted or thefloating-point format being adjusted to properly capture the dynamicrange of the data within each block. Data to track the adjustments tothe data and data format can be tracked via the metadata 2101, 2104,2105, 2108.

While a 64×64 tensor 2100 is illustrated as having four 32×32 blocks2102, 2103, 2106, 2107, embodiments are not limited to the illustratedtensor size and block size, and any tensor and block size can be used invarious embodiments.

FIG. 21B illustrates a blocked multi-dimensional fine-grained quantizedtensor 2110, according to an embodiment. The tensor 2110 can bepartitioned into blocks along one or more dimensions, with each blockhaving a separate shared exponent (e.g., EXP1 2111, EXP2, 2112, etc.).The shared exponent data for the quantized tensor 2110 can be stored inmetadata for the tensor. The metadata can maintain the exponent scalingfactor for each block, as well as the block size for each block. In oneembodiment, partitioning with variable block sizes can be performedalong all dimensions of the tensor. A data representation can begenerated that has variable size blocks in all dimensions of the tensor.For example, tensor data for use in a convolution operation can have N,C, H, and W dimensions corresponding sample, color channel, width, andheight dimensions. Partitioning with variable block sizes can beperformed along all dimensions of the tensor. Additionally, GEMM(general matrix multiply) kernels can have partitioning along M, N, andK dimensions that specify the number of rows and columns of matrices Aand B to be multiplied (e.g., with M being the number of rows in matrixA, N being the number of columns in matrix B, and K being the number ofcolumns in matrix A and the number of rows in matrix B). Multipledifferent types of blocking and clustering algorithms can be used todetermine the block sizes for a tensor. Furthermore, tensor data can beblocked in a contiguous or non-contiguous manner using variable ornon-uniform block sizes. Additionally, while blocked dynamic fixed-pointrepresentations are described herein, the blocked data representationcan also be used for low precision floating-point data.

FIG. 21C shows an exemplary group 2140 of 3×3 filters that can berepresented in a blocked dynamic-precision data type. The group 2140 offilters contains (R×S) sub groups of n elements from contiguous filtersalong the C dimension. The exemplary group 2140 can be described byequation 2142, which states W^((k))={w_(i) ^(j)}={w₀ ⁰, w₁ ⁰, w₂ ⁰, . .. , w_(N) ⁰}, α_(k)={α_(k) ^(j)}={α_(k) ^(j), . . . }, i=1 . . . N, j=1. . . (R×S). N filters 2144A-2144N (w₀ . . . w_(N)) contain ternarizedweights that are derived based on full precision weights. For each group2140 of 3×3 filters, there will be a 3×3 filter containing a scalingfactor 2146 (a) for each element in the filter. Hardware logic can beconfigured to perform low-precision computations using blocked, dynamicprecision data. In one embodiment, the filter data can be quantized to ablocked, dynamic precision format that requires a reduced number of bitsto store the data, while shifting the scale factor in a fine-grainedmanner to avoid data loss. The activations at each layer can bequantized to a low-precision format, such as a dynamic fixed-point orblocked flow-precision floating-point format.

As illustrated in FIG. 21D, computational logic 2130 to perform aprocess of implementing block dynamic fixed-point can receive an inputtensor associated with a matrix operation to be performed oncomputational logic, as shown at block 2132. The computational logic canthen divide the input tensor into multiple blocks, as shown at block2134. For each of the multiple blocks, the computational logic candetermine a shared exponent (e.g., scale factor) for each of themultiple blocks and concert each block to dynamic fixed-point, as shownat block 2136. The computational logic can then store metadata for themultiple blocks to indicate a data format for the shared exponent forthe multiple blocks, as shown at block 2138. In one embodiment, themetadata for the multiple blocks is stored in a separate metadatasegment in memory. In one embodiment, metadata for each of the multipleblocks is appended to or located adjacent to each block.

Once metadata is written for the input tensor, the computational logiccan perform a matrix operation using the divided dynamic fixed-pointinput tensor, as shown at block 2139. In some embodiments, thecomputational logic includes hardware support for dynamic fixed-point.In one embodiment. metadata describing the data format of a block of theinput tensor is stored at a pre-determined memory position relative tothe tensor data. The hardware can automatically read the metadata for ablock of the tensor and configure the logic units of the hardware toprocess the tensor data at the specified dynamic fixed-pointconfiguration. For example, logic unit 1920 as in FIG. 19A, logic unit1940 as in FIG. 19B, or a similar logic unit, can be configured toprocess tensor data in a dynamic fixed-point format.

Mixed precision training can be performed using low precision dynamicfixed-point data types to train a neural network at an accuracy similarto that of 32-bit floating point. One embodiment provides for a 16-bitdynamic fixed-point implementation, which was tested in trainingrelative to training performed using FP32. Training was performed forseveral state of the art ImageNet-class CNNs using variants of theBerkeley Vision and Learning Center (BVLC) Caffe framework, with onevariant modified add DFP16 data-type support. Using hardwareacceleration of DFP16 compute primitives, an average of 1.8× speedup intraining throughput was realize relative to baseline FP32 performance,with accuracy results as shown in Table 6 below.

TABLE 6 Training Configuration and ImageNet-1K Classification AccuracyMixed Precision Batch-size/ Baseline DFP16 Models Epochs Top-1 Top-5Top-1 Top-5 Resnet-50 1024/90 75.70% 92.78% 75.77% 92.84% GoogLeNet-v11024/80 69.26% 89.31% 69.34% 89.31% VGG-16  256/60 68.23% 88.47% 68.12%88.18% AlexNet 1024/88 57.43% 80.65% 56.94% 80.06%

As shown in Table 6 above, ImageNet-1K networks trained with DFP16achieve state of the art accuracy, with some models exceeding theaccuracy of the FP32 baseline. Convergence for DFP16 training tracksclosely with FP32 training while realizing a potential of as much as 2×savings in computation, communication, and storage. While CNN trainingis illustrated, the techniques described herein can also be applied toother types of neural networks, such as RNNs, LSTM, and GANs (generativeadversarial networks).

Additional Exemplary Graphics Processing System

Details of the embodiments described above can be incorporated withingraphics processing systems and devices described below. The graphicsprocessing system and devices of FIG. 22-35 illustrate alternativesystems and graphics processing hardware that can implement any and allof the techniques described above.

FIG. 22 is a block diagram of a processing system 2200, according to anembodiment. In various embodiments, the system 2200 includes one or moreprocessors 2202 and one or more graphics processors 2208, and may be asingle processor desktop system, a multiprocessor workstation system, ora server system having a large number of processors 2202 or processorcores 2207. In one embodiment, the system 2200 is a processing platformincorporated within a system-on-a-chip (SoC) integrated circuit for usein mobile, handheld, or embedded devices.

An embodiment of system 2200 can include or be incorporated within aserver-based gaming platform, a game console, including a game and mediaconsole, a mobile gaming console, a handheld game console, or an onlinegame console. In some embodiments system 2200 is a mobile phone, smartphone, tablet computing device or mobile Internet device. Dataprocessing system 2200 can also include, couple with, or be integratedwithin a wearable device, such as a smart watch wearable device, smarteyewear device, augmented reality device, or virtual reality device. Insome embodiments, data processing system 2200 is a television or set topbox device having one or more processors 2202 and a graphical interfacegenerated by one or more graphics processors 2208.

In some embodiments, the one or more processors 2202 each include one ormore processor cores 2207 to process instructions which, when executed,perform operations for system and user software. In some embodiments,each of the one or more processor cores 2207 is configured to process aspecific instruction set 2209. In some embodiments, instruction set 2209may facilitate Complex Instruction Set Computing (CISC), ReducedInstruction Set Computing (RISC), or computing via a Very LongInstruction Word (VLIW). Multiple processor cores 2207 may each processa different instruction set 2209, which may include instructions tofacilitate the emulation of other instruction sets. Processor core 2207may also include other processing devices, such a Digital SignalProcessor (DSP).

In some embodiments, the processor 2202 includes cache memory 2204.Depending on the architecture, the processor 2202 can have a singleinternal cache or multiple levels of internal cache. In someembodiments, the cache memory is shared among various components of theprocessor 2202. In some embodiments, the processor 2202 also uses anexternal cache (e.g., a Level-3 (L3) cache or Last Level Cache (LLC))(not shown), which may be shared among processor cores 2207 using knowncache coherency techniques. A register file 2206 is additionallyincluded in processor 2202 which may include different types ofregisters for storing different types of data (e.g., integer registers,floating-point registers, status registers, and an instruction pointerregister). Some registers may be general-purpose registers, while otherregisters may be specific to the design of the processor 2202.

In some embodiments, processor 2202 is coupled with a processor bus 2210to transmit communication signals such as address, data, or controlsignals between processor 2202 and other components in system 2200. Inone embodiment, the system 2200 uses an exemplary ‘hub’ systemarchitecture, including a memory controller hub 2216 and an Input Output(I/O) controller hub 2230. A memory controller hub 2216 facilitatescommunication between a memory device and other components of system2200, while an I/O Controller Hub (ICH) 2230 provides connections to I/Odevices via a local I/O bus. In one embodiment, the logic of the memorycontroller hub 2216 is integrated within the processor.

Memory device 2220 can be a dynamic random-access memory (DRAM) device,a static random-access memory (SRAM) device, flash memory device,phase-change memory device, or some other memory device having suitableperformance to serve as process memory. In one embodiment, the memorydevice 2220 can operate as system memory for the system 2200, to storedata 2222 and instructions 2221 for use when the one or more processors2202 executes an application or process. Memory controller hub 2216 alsocouples with an optional external graphics processor 2212, which maycommunicate with the one or more graphics processors 2208 in processors2202 to perform graphics and media operations.

In some embodiments, ICH 2230 enables peripherals to connect to memorydevice 2220 and processor 2202 via a high-speed I/O bus. The I/Operipherals include, but are not limited to, an audio controller 2246, afirmware interface 2228, a wireless transceiver 2226 (e.g., Wi-Fi,Bluetooth), a data storage device 2224 (e.g., hard disk drive, flashmemory, etc.), and a legacy I/O controller 2240 for coupling legacy(e.g., Personal System 2 (PS/2)) devices to the system. One or moreUniversal Serial Bus (USB) controllers 2242 connect input devices, suchas keyboard and mouse 2244 combinations. A network controller 2234 mayalso couple with ICH 2230. In some embodiments, a high-performancenetwork controller (not shown) couples with processor bus 2210. It willbe appreciated that the system 2200 shown is exemplary and not limiting,as other types of data processing systems that are differentlyconfigured may also be used. For example, the I/O controller hub 2230may be integrated within the one or more processor 2202, or the memorycontroller hub 2216 and I/O controller hub 2230 may be integrated into adiscreet external graphics processor, such as the external graphicsprocessor 2212.

FIG. 23 is a block diagram of a processor 2300 having one or moreprocessor cores 2302A-2302N, an integrated memory controller 2314, andan integrated graphics processor 2308. Those elements of FIG. 23 havingthe same reference numbers (or names) as the elements of any otherfigure herein can operate or function in any manner similar to thatdescribed elsewhere herein but are not limited to such. Processor 2300can include additional cores up to and including additional core 2302Nrepresented by the dashed lined boxes. Each of processor cores2302A-2302N includes one or more internal cache units 2304A-2304N. Insome embodiments, each processor core also has access to one or moreshared cached units 2306.

The internal cache units 2304A-2304N and shared cache units 2306represent a cache memory hierarchy within the processor 2300. The cachememory hierarchy may include at least one level of instruction and datacache within each processor core and one or more levels of sharedmid-level cache, such as a Level 2 (L2), Level 3 (L3), Level 4 (L4), orother levels of cache, where the highest level of cache before externalmemory is classified as the LLC. In some embodiments, cache coherencylogic maintains coherency between the various cache units 2306 and2304A-2304N.

In some embodiments, processor 2300 may also include a set of one ormore bus controller units 2316 and a system agent core 2310. The one ormore bus controller units 2316 manage a set of peripheral buses, such asone or more Peripheral Component Interconnect buses (e.g., PCI, PCIExpress). System agent core 2310 provides management functionality forthe various processor components. In some embodiments, system agent core2310 includes one or more integrated memory controllers 2314 to manageaccess to various external memory devices (not shown).

In some embodiments, one or more of the processor cores 2302A-2302Ninclude support for simultaneous multi-threading. In such embodiment,the system agent core 2310 includes components for coordinating andoperating cores 2302A-2302N during multi-threaded processing. Systemagent core 2310 may additionally include a power control unit (PCU),which includes logic and components to regulate the power state ofprocessor cores 2302A-2302N and graphics processor 2308.

In some embodiments, processor 2300 additionally includes graphicsprocessor 2308 to execute graphics processing operations. In someembodiments, the graphics processor 2308 couples with the set of sharedcache units 2306, and the system agent core 2310, including the one ormore integrated memory controllers 2314. In some embodiments, a displaycontroller 2311 is coupled with the graphics processor 2308 to drivegraphics processor output to one or more coupled displays. In someembodiments, display controller 2311 may be a separate module coupledwith the graphics processor via at least one interconnect or may beintegrated within the graphics processor 2308 or system agent core 2310.

In some embodiments, a ring-based interconnect unit 2312 is used tocouple the internal components of the processor 2300. However, analternative interconnect unit may be used, such as a point-to-pointinterconnect, a switched interconnect, or other techniques, includingtechniques well known in the art. In some embodiments, graphicsprocessor 2308 couples with the ring interconnect 2312 via an I/O link2313.

The exemplary I/O link 2313 represents at least one of multiplevarieties of I/O interconnects, including an on package I/O interconnectwhich facilitates communication between various processor components anda high-performance embedded memory module 2318, such as an eDRAM module.In some embodiments, each of the processor cores 2302A-2302N andgraphics processor 2308 use embedded memory modules 2318 as a sharedLast Level Cache.

In some embodiments, processor cores 2302A-2302N are homogenous coresexecuting the same instruction set architecture. In another embodiment,processor cores 2302A-2302N are heterogeneous in terms of instructionset architecture (ISA), where one or more of processor cores 2302A-2302Nexecute a first instruction set, while at least one of the other coresexecutes a subset of the first instruction set or a differentinstruction set. In one embodiment processor cores 2302A-2302N areheterogeneous in terms of microarchitecture, where one or more coreshaving a relatively higher power consumption couple with one or morepower cores having a lower power consumption. Additionally, processor2300 can be implemented on one or more chips or as an SoC integratedcircuit having the illustrated components, in addition to othercomponents.

FIG. 24 is a block diagram of a graphics processor 2400, which may be adiscrete graphics processing unit, or may be a graphics processorintegrated with a plurality of processing cores. In some embodiments,the graphics processor communicates via a memory mapped I/O interface toregisters on the graphics processor and with commands placed into theprocessor memory. In some embodiments, graphics processor 2400 includesa memory interface 2414 to access memory. Memory interface 2414 can bean interface to local memory, one or more internal caches, one or moreshared external caches, and/or to system memory.

In some embodiments, graphics processor 2400 also includes a displaycontroller 2402 to drive display output data to a display device 2420.Display controller 2402 includes hardware for one or more overlay planesfor the display and composition of multiple layers of video or userinterface elements. In some embodiments, graphics processor 2400includes a video codec engine 2406 to encode, decode, or transcode mediato, from, or between one or more media encoding formats, including, butnot limited to Moving Picture Experts Group (MPEG) formats such asMPEG-2, Advanced Video Coding (AVC) formats such as H.264/MPEG-4 AVC, aswell as the Society of Motion Picture & Television Engineers (SMPTE)421M/VC-1, and Joint Photographic Experts Group (JPEG) formats such asJPEG, and Motion JPEG (MJPEG) formats.

In some embodiments, graphics processor 2400 includes a block imagetransfer (BLIT) engine 2404 to perform two-dimensional (2D) rasterizeroperations including, for example, bit-boundary block transfers.However, in one embodiment, 2D graphics operations are performed usingone or more components of graphics processing engine (GPE) 2410. In someembodiments, GPE 2410 is a compute engine for performing graphicsoperations, including three-dimensional (3D) graphics operations andmedia operations.

In some embodiments, GPE 310 includes a 3D pipeline 2412 for performing3D operations, such as rendering three-dimensional images and scenesusing processing functions that act upon 3D primitive shapes (e.g.,rectangle, triangle, etc.). The 3D pipeline 2412 includes programmableand fixed function elements that perform various tasks within theelement and/or spawn execution threads to a 3D/Media sub-system 2415.While 3D pipeline 2412 can be used to perform media operations, anembodiment of GPE 2410 also includes a media pipeline 2416 that isspecifically used to perform media operations, such as videopost-processing and image enhancement.

In some embodiments, media pipeline 2416 includes fixed function orprogrammable logic units to perform one or more specialized mediaoperations, such as video decode acceleration, video de-interlacing, andvideo encode acceleration in place of, or on behalf of video codecengine 2406. In some embodiments, media pipeline 2416 additionallyincludes a thread spawning unit to spawn threads for execution on3D/Media sub-system 2415. The spawned threads perform computations forthe media operations on one or more graphics execution units included in3D/Media sub-system 2415.

In some embodiments, 3D/Media subsystem 2415 includes logic forexecuting threads spawned by 3D pipeline 2412 and media pipeline 2416.In one embodiment, the pipelines send thread execution requests to3D/Media subsystem 2415, which includes thread dispatch logic forarbitrating and dispatching the various requests to available threadexecution resources. The execution resources include an array ofgraphics execution units to process the 3D and media threads. In someembodiments, 3D/Media subsystem 2415 includes one or more internalcaches for thread instructions and data. In some embodiments, thesubsystem also includes shared memory, including registers andaddressable memory, to share data between threads and to store outputdata.

Additional Exemplary Graphics Processing Engine

FIG. 25 is a block diagram of a graphics processing engine 2510 of agraphics processor in accordance with some embodiments. In oneembodiment, the graphics processing engine (GPE) 2510 is a version ofthe GPE 2410 shown in FIG. 24. Elements of FIG. 25 having the samereference numbers (or names) as the elements of any other figure hereincan operate or function in any manner similar to that describedelsewhere herein but are not limited to such. For example, the 3Dpipeline 2412 and media pipeline 2416 of FIG. 24 are illustrated. Themedia pipeline 2416 is optional in some embodiments of the GPE 2510 andmay not be explicitly included within the GPE 2510. For example, and inat least one embodiment, a separate media and/or image processor iscoupled to the GPE 2510.

In some embodiments, GPE 2510 couples with or includes a commandstreamer 2503, which provides a command stream to the 3D pipeline 2412and/or media pipelines 2416. In some embodiments, command streamer 2503is coupled with memory, which can be system memory, or one or more ofinternal cache memory and shared cache memory. In some embodiments,command streamer 2503 receives commands from the memory and sends thecommands to 3D pipeline 2412 and/or media pipeline 2416. The commandsare directives fetched from a ring buffer, which stores commands for the3D pipeline 2412 and media pipeline 2416. In one embodiment, the ringbuffer can additionally include batch command buffers storing batches ofmultiple commands. The commands for the 3D pipeline 2412 can alsoinclude references to data stored in memory, such as but not limited tovertex and geometry data for the 3D pipeline 2412 and/or image data andmemory objects for the media pipeline 2416. The 3D pipeline 2412 andmedia pipeline 2416 process the commands and data by performingoperations via logic within the respective pipelines or by dispatchingone or more execution threads to a graphics core array 2514.

In various embodiments, the 3D pipeline 2412 can execute one or moreshader programs, such as vertex shaders, geometry shaders, pixelshaders, fragment shaders, compute shaders, or other shader programs, byprocessing the instructions and dispatching execution threads to thegraphics core array 2514. The graphics core array 2514 provides aunified block of execution resources. Multi-purpose execution logic(e.g., execution units) within the graphic core array 2514 includessupport for various 3D API shader languages and can execute multiplesimultaneous execution threads associated with multiple shaders.

In some embodiments, the graphics core array 2514 also includesexecution logic to perform media functions, such as video and/or imageprocessing. In one embodiment, the execution units additionally includegeneral-purpose logic that is programmable to perform parallel generalpurpose computational operations, in addition to graphics processingoperations. The general-purpose logic can perform processing operationsin parallel or in conjunction with general purpose logic within theprocessor core(s) 2207 of FIG. 22 or core 2302A-2302N as in FIG. 23.

Output data generated by threads executing on the graphics core array2514 can output data to memory in a unified return buffer (URB) 2518.The URB 2518 can store data for multiple threads. In some embodiments,the URB 2518 may be used to send data between different threadsexecuting on the graphics core array 2514. In some embodiments, the URB2518 may additionally be used for synchronization between threads on thegraphics core array and fixed function logic within the shared functionlogic 2520.

In some embodiments, graphics core array 2514 is scalable, such that thearray includes a variable number of graphics cores, each having avariable number of execution units based on the target power andperformance level of GPE 2510. In one embodiment, the executionresources are dynamically scalable, such that execution resources may beenabled or disabled as needed.

The graphics core array 2514 couples with shared function logic 2520that includes multiple resources that are shared between the graphicscores in the graphics core array. The shared functions within the sharedfunction logic 2520 are hardware logic units that provide specializedsupplemental functionality to the graphics core array 2514. In variousembodiments, shared function logic 2520 includes but is not limited tosampler 2521, math 2522, and inter-thread communication (ITC) 2523logic. Additionally, some embodiments implement one or more cache(s)2525 within the shared function logic 2520. A shared function isimplemented where the demand for a given specialized function isinsufficient for inclusion within the graphics core array 2514. Insteada single instantiation of that specialized function is implemented as astand-alone entity in the shared function logic 2520 and shared amongthe execution resources within the graphics core array 2514. The preciseset of functions that are shared between the graphics core array 2514and included within the graphics core array 2514 varies betweenembodiments.

FIG. 26 is a block diagram of a graphics processor 2600 provided by anadditional embodiment. Elements of FIG. 26 having the same referencenumbers (or names) as the elements of any other figure herein canoperate or function in any manner similar to that described elsewhereherein but are not limited to such.

In some embodiments, graphics processor 2600 includes a ringinterconnect 2602, a pipeline front-end 2604, a media engine 2637, andgraphics cores 2680A-2680N. In some embodiments, ring interconnect 2602couples the graphics processor to other processing units, includingother graphics processors or one or more general-purpose processorcores. In some embodiments, the graphics processor is one of manyprocessors integrated within a multi-core processing system.

In some embodiments, graphics processor 2600 receives batches ofcommands via ring interconnect 2602. The incoming commands areinterpreted by a command streamer 2603 in the pipeline front-end 2604.In some embodiments, graphics processor 2600 includes scalable executionlogic to perform 3D geometry processing and media processing via thegraphics core(s) 2680A-2680N. For 3D geometry processing commands,command streamer 2603 supplies commands to geometry pipeline 2636. Forat least some media processing commands, command streamer 2603 suppliesthe commands to a video front end 2634, which couples with a mediaengine 2637. In some embodiments, media engine 2637 includes a VideoQuality Engine (VQE) 2630 for video and image post-processing and amulti-format encode/decode (MFX) 2633 engine to providehardware-accelerated media data encode and decode. In some embodiments,geometry pipeline 2636 and media engine 2637 each generate executionthreads for the thread execution resources provided by at least onegraphics core 2680A.

In some embodiments, graphics processor 2600 includes scalable threadexecution resources featuring modular cores 2680A-2680N (sometimesreferred to as core slices), each having multiple sub-cores 2650A-550N,2660A-2660N (sometimes referred to as core sub-slices). In someembodiments, graphics processor 2600 can have any number of graphicscores 2680A through 2680N. In some embodiments, graphics processor 2600includes a graphics core 2680A having at least a first sub-core 2650Aand a second sub-core 2660A. In other embodiments, the graphicsprocessor is a low power processor with a single sub-core (e.g., 2650A).In some embodiments, graphics processor 2600 includes multiple graphicscores 2680A-2680N, each including a set of first sub-cores 2650A-2650Nand a set of second sub-cores 2660A-2660N. Each sub-core in the set offirst sub-cores 2650A-2650N includes at least a first set of executionunits 2652A-2652N and media/texture samplers 2654A-2654N. Each sub-corein the set of second sub-cores 2660A-2660N includes at least a secondset of execution units 2662A-2662N and samplers 2664A-2664N. In someembodiments, each sub-core 2650A-2650N, 2660A-2660N shares a set ofshared resources 2670A-2670N. In some embodiments, the shared resourcesinclude shared cache memory and pixel operation logic. Other sharedresources may also be included in the various embodiments of thegraphics processor.

Additional Exemplary Execution Units

FIG. 27 illustrates thread execution logic 2700 including an array ofprocessing elements employed in some embodiments. Elements of FIG. 27having the same reference numbers (or names) as the elements of anyother figure herein can operate or function in any manner similar tothat described elsewhere herein but are not limited to such.

In some embodiments, thread execution logic 2700 includes a shaderprocessor 2702, a thread dispatcher 2704, instruction cache 2706, ascalable execution unit array including a plurality of execution units2708A-2708N, a sampler 2710, a data cache 2712, and a data port 2714. Inone embodiment, the scalable execution unit array can dynamically scaleby enabling or disabling one or more execution units (e.g., any ofexecution unit 2708A, 2708B, 2708C, 2708D, through 2708N-1 and 2708N)based on the computational requirements of a workload. In oneembodiment, the included components are interconnected via aninterconnect fabric that links to each of the components. In someembodiments, thread execution logic 2700 includes one or moreconnections to memory, such as system memory or cache memory, throughone or more of instruction cache 2706, data port 2714, sampler 2710, andexecution units 2708A-2708N. In some embodiments, each execution unit(e.g. 2708A) is a stand-alone programmable general purpose computationalunit that is capable of executing multiple simultaneous hardware threadswhile processing multiple data elements in parallel for each thread. Invarious embodiments, the array of execution units 2708A-2708N isscalable to include any number individual execution units.

In some embodiments, the execution units 2708A-2708N are primarily usedto execute shader programs. A shader processor 2702 can process thevarious shader programs and dispatch execution threads associated withthe shader programs via a thread dispatcher 2704. In one embodiment, thethread dispatcher includes logic to arbitrate thread initiation requestsfrom the graphics and media pipelines and instantiate the requestedthreads on one or more execution unit in the execution units2708A-2708N. For example, the geometry pipeline (e.g., 2636 of FIG. 26)can dispatch vertex, tessellation, or geometry shaders to the threadexecution logic 2700 (FIG. 27) for processing. In some embodiments,thread dispatcher 2704 can also process runtime thread spawning requestsfrom the executing shader programs.

In some embodiments, the execution units 2708A-2708N support aninstruction set that includes native support for many standard 3Dgraphics shader instructions, such that shader programs from graphicslibraries (e.g., Direct 3D and OpenGL) are executed with a minimaltranslation. The execution units support vertex and geometry processing(e.g., vertex programs, geometry programs, vertex shaders), pixelprocessing (e.g., pixel shaders, fragment shaders) and general-purposeprocessing (e.g., compute and media shaders). Each of the executionunits 2708A-2708N is capable of multi-issue single instruction multipledata (SIMD) execution and multi-threaded operation enables an efficientexecution environment in the face of higher latency memory accesses.Each hardware thread within each execution unit has a dedicatedhigh-bandwidth register file and associated independent thread-state.Execution is multi-issue per clock to pipelines capable of integer,single and double precision floating-point operations, SIMD branchcapability, logical operations, transcendental operations, and othermiscellaneous operations. While waiting for data from memory or one ofthe shared functions, dependency logic within the execution units2708A-2708N causes a waiting thread to sleep until the requested datahas been returned. While the waiting thread is sleeping, hardwareresources may be devoted to processing other threads. For example,during a delay associated with a vertex shader operation, an executionunit can perform operations for a pixel shader, fragment shader, oranother type of shader program, including a different vertex shader.

Each execution unit in execution units 2708A-2708N operates on arrays ofdata elements. The number of data elements is the “execution size,” orthe number of channels for the instruction. An execution channel is alogical unit of execution for data element access, masking, and flowcontrol within instructions. The number of channels may be independentof the number of physical arithmetic logic units (ALUs) orfloating-point units (FPUs) for a particular graphics processor. In someembodiments, execution units 2708A-2708N support integer andfloating-point data types.

The execution unit instruction set includes SIMD instructions. Thevarious data elements can be stored as a packed data type in a registerand the execution unit will process the various elements based on thedata size of the elements. For example, when operating on a 256-bit widevector, the 256 bits of the vector are stored in a register and theexecution unit operates on the vector as four separate 64-bit packeddata elements (Quad-Word (QW) size data elements), eight separate 32-bitpacked data elements (Double Word (DW) size data elements), sixteenseparate 16-bit packed data elements (Word (W) size data elements), orthirty-two separate 8-bit data elements (byte (B) size data elements).However, different vector widths and register sizes are possible.

One or more internal instruction caches (e.g., 2706) are included in thethread execution logic 2700 to cache thread instructions for theexecution units. In some embodiments, one or more data caches (e.g.,2712) are included to cache thread data during thread execution. In someembodiments, a sampler 2710 is included to provide texture sampling for3D operations and media sampling for media operations. In someembodiments, sampler 2710 includes specialized texture or media samplingfunctionality to process texture or media data during the samplingprocess before providing the sampled data to an execution unit.

During execution, the graphics and media pipelines send threadinitiation requests to thread execution logic 2700 via thread spawningand dispatch logic. Once a group of geometric objects has been processedand rasterized into pixel data, pixel processor logic (e.g., pixelshader logic, fragment shader logic, etc.) within the shader processor2702 is invoked to further compute output information and cause resultsto be written to output surfaces (e.g., color buffers, depth buffers,stencil buffers, etc.). In some embodiments, a pixel shader or fragmentshader calculates the values of the various vertex attributes that areto be interpolated across the rasterized object. In some embodiments,pixel processor logic within the shader processor 2702 then executes anapplication programming interface (API)-supplied pixel or fragmentshader program. To execute the shader program, the shader processor 2702dispatches threads to an execution unit (e.g., 2708A) via threaddispatcher 2704. In some embodiments, pixel shader 2702 uses texturesampling logic in the sampler 2710 to access texture data in texturemaps stored in memory. Arithmetic operations on the texture data and theinput geometry data compute pixel color data for each geometric fragmentor discards one or more pixels from further processing.

In some embodiments, the data port 2714 provides a memory accessmechanism for the thread execution logic 2700 output processed data tomemory for processing on a graphics processor output pipeline. In someembodiments, the data port 2714 includes or couples to one or more cachememories (e.g., data cache 2712) to cache data for memory access via thedata port.

FIG. 28 is a block diagram illustrating a graphics processor instructionformats 2800 according to some embodiments. In one or more embodiment,the graphics processor execution units support an instruction set havinginstructions in multiple formats. The solid lined boxes illustrate thecomponents that are generally included in an execution unit instruction,while the dashed lines include components that are optional or that areonly included in a sub-set of the instructions. In some embodiments,instruction format 2800 described and illustrated aremacro-instructions, in that they are instructions supplied to theexecution unit, as opposed to micro-operations resulting frominstruction decode once the instruction is processed.

In some embodiments, the graphics processor execution units nativelysupport instructions in a 128-bit instruction format 2810. A 64-bitcompacted instruction format 2830 is available for some instructionsbased on the selected instruction, instruction options, and number ofoperands. The native 128-bit instruction format 2810 provides access toall instruction options, while some options and operations arerestricted in the 64-bit format 2830. The native instructions availablein the 64-bit format 2830 vary by embodiment. In some embodiments, theinstruction is compacted in part using a set of index values in an indexfield 2813. The execution unit hardware references a set of compactiontables based on the index values and uses the compaction table outputsto reconstruct a native instruction in the 128-bit instruction format2810.

For each format, instruction opcode 2812 defines the operation that theexecution unit is to perform. The execution units execute eachinstruction in parallel across the multiple data elements of eachoperand. For example, in response to an add instruction the executionunit performs a simultaneous add operation across each color channelrepresenting a texture element or picture element. By default, theexecution unit performs each instruction across all data channels of theoperands. In some embodiments, instruction control field 2814 enablescontrol over certain execution options, such as channels selection(e.g., predication) and data channel order (e.g., swizzle). Forinstructions in the 128-bit instruction format 2810 an exec-size field2816 limits the number of data channels that will be executed inparallel. In some embodiments, exec-size field 2816 is not available foruse in the 64-bit compact instruction format 2830.

Some execution unit instructions have up to three operands including twosource operands, src0 2820, src1 2822, and one destination 2818. In someembodiments, the execution units support dual destination instructions,where one of the destinations is implied. Data manipulation instructionscan have a third source operand (e.g., SRC2 2824), where the instructionopcode 2812 determines the number of source operands. An instruction'slast source operand can be an immediate (e.g., hard-coded) value passedwith the instruction.

In some embodiments, the 128-bit instruction format 2810 includes anaccess/address mode field 2826 specifying, for example, whether directregister addressing mode or indirect register addressing mode is used.When direct register addressing mode is used, the register address ofone or more operands is directly provided by bits in the instruction.

In some embodiments, the 128-bit instruction format 2810 includes anaccess/address mode field 2826, which specifies an address mode and/oran access mode for the instruction. In one embodiment, the access modeis used to define a data access alignment for the instruction. Someembodiments support access modes including a 16-byte aligned access modeand a 1-byte aligned access mode, where the byte alignment of the accessmode determines the access alignment of the instruction operands. Forexample, when in a first mode, the instruction may use byte-alignedaddressing for source and destination operands and when in a secondmode, the instruction may use 16-byte-aligned addressing for all sourceand destination operands.

In one embodiment, the address mode portion of the access/address modefield 2826 determines whether the instruction is to use direct orindirect addressing. When direct register addressing mode is used bitsin the instruction directly provide the register address of one or moreoperands. When indirect register addressing mode is used, the registeraddress of one or more operands may be computed based on an addressregister value and an address immediate field in the instruction.

In some embodiments instructions are grouped based on opcode 2812bit-fields to simplify Opcode decode 2840. For an 8-bit opcode, bits 4,5, and 6 allow the execution unit to determine the type of opcode. Theprecise opcode grouping shown is merely an example. In some embodiments,a move and logic opcode group 2842 includes data movement and logicinstructions (e.g., move (mov), compare (cmp)). In some embodiments,move and logic group 2842 shares the five most significant bits (MSB),where move (mov) instructions are in the form of 0000xxxxb and logicinstructions are in the form of 0001xxxxb. A flow control instructiongroup 2844 (e.g., call, jump (jmp)) includes instructions in the form of0010xxxxb (e.g., 0x20). A miscellaneous instruction group 2846 includesa mix of instructions, including synchronization instructions (e.g.,wait, send) in the form of 0011xxxxb (e.g., 0x30). A parallel mathinstruction group 2848 includes component-wise arithmetic instructions(e.g., add, multiply (mul)) in the form of 0100xxxxb (e.g., 0x40). Theparallel math group 2848 performs the arithmetic operations in parallelacross data channels. The vector math group 2850 includes arithmeticinstructions (e.g., dp4) in the form of 0101xxxxb (e.g., 0x50). Thevector math group performs arithmetic such as dot product calculationson vector operands.

Additional Exemplary Graphics Pipeline

FIG. 29 is a block diagram of a graphics processor 2900 according toanother embodiment. Elements of FIG. 29 having the same referencenumbers (or names) as the elements of any other figure herein canoperate or function in any manner similar to that described elsewhereherein but are not limited to such.

In some embodiments, graphics processor 2900 includes a graphicspipeline 2920, a media pipeline 2930, a display engine 2940, threadexecution logic 2950, and a render output pipeline 2970. In someembodiments, graphics processor 2900 is a graphics processor within amulti-core processing system that includes one or more general purposeprocessing cores. The graphics processor is controlled by registerwrites to one or more control registers (not shown) or via commandsissued to graphics processor 2900 via a ring interconnect 2902. In someembodiments, ring interconnect 2902 couples graphics processor 2900 toother processing components, such as other graphics processors orgeneral-purpose processors. Commands from ring interconnect 2902 areinterpreted by a command streamer 2903, which supplies instructions toindividual components of graphics pipeline 2920 or media pipeline 2930.

In some embodiments, command streamer 2903 directs the operation of avertex fetcher 2905 that reads vertex data from memory and executesvertex-processing commands provided by command streamer 2903. In someembodiments, vertex fetcher 2905 provides vertex data to a vertex shader2907, which performs coordinate space transformation and lightingoperations to each vertex. In some embodiments, vertex fetcher 2905 andvertex shader 2907 execute vertex-processing instructions by dispatchingexecution threads to execution units 2952A-2952B via a thread dispatcher2931.

In some embodiments, execution units 2952A-2952B are an array of vectorprocessors having an instruction set for performing graphics and mediaoperations. In some embodiments, execution units 2952A-2952B have anattached L1 cache 2951 that is specific for each array or shared betweenthe arrays. The cache can be configured as a data cache, an instructioncache, or a single cache that is partitioned to contain data andinstructions in different partitions.

In some embodiments, graphics pipeline 2920 includes tessellationcomponents to perform hardware-accelerated tessellation of 3D objects.In some embodiments, a programmable hull shader 811 configures thetessellation operations. A programmable domain shader 817 providesback-end evaluation of tessellation output. A tessellator 2913 operatesat the direction of hull shader 2911 and contains special purpose logicto generate a set of detailed geometric objects based on a coarsegeometric model that is provided as input to graphics pipeline 2920. Insome embodiments, if tessellation is not used, tessellation components(e.g., hull shader 2911, tessellator 2913, and domain shader 2917) canbe bypassed.

In some embodiments, complete geometric objects can be processed by ageometry shader 2919 via one or more threads dispatched to executionunits 2952A-2952B or can proceed directly to the clipper 2929. In someembodiments, the geometry shader operates on entire geometric objects,rather than vertices or patches of vertices as in previous stages of thegraphics pipeline. If the tessellation is disabled, the geometry shader2919 receives input from the vertex shader 2907. In some embodiments,geometry shader 2919 is programmable by a geometry shader program toperform geometry tessellation if the tessellation units are disabled.

Before rasterization, a clipper 2929 processes vertex data. The clipper2929 may be a fixed function clipper or a programmable clipper havingclipping and geometry shader functions. In some embodiments, arasterizer and depth test component 2973 in the render output pipeline2970 dispatches pixel shaders to convert the geometric objects intotheir per pixel representations. In some embodiments, pixel shader logicis included in thread execution logic 2950. In some embodiments, anapplication can bypass the rasterizer and depth test component 2973 andaccess un-rasterized vertex data via a stream out unit 2923.

The graphics processor 2900 has an interconnect bus, interconnectfabric, or some other interconnect mechanism that allows data andmessage passing amongst the major components of the processor. In someembodiments, execution units 2952A-2952B and associated cache(s) 2951,texture and media sampler 2954, and texture/sampler cache 2958interconnect via a data port 2956 to perform memory access andcommunicate with render output pipeline components of the processor. Insome embodiments, sampler 2954, caches 2951, 2958 and execution units2952A-2952B each have separate memory access paths.

In some embodiments, render output pipeline 2970 contains a rasterizerand depth test component 2973 that converts vertex-based objects into anassociated pixel-based representation. In some embodiments, therasterizer logic includes a windower/masker unit to perform fixedfunction triangle and line rasterization. An associated render cache2978 and depth cache 2979 are also available in some embodiments. Apixel operations component 2977 performs pixel-based operations on thedata, though in some instances, pixel operations associated with 2Doperations (e.g. bit block image transfers with blending) are performedby the 2D engine 2941 or substituted at display time by the displaycontroller 2943 using overlay display planes. In some embodiments, ashared L3 cache 2975 is available to all graphics components, allowingthe sharing of data without the use of main system memory.

In some embodiments, graphics processor media pipeline 2930 includes amedia engine 2937 and a video front end 2934. In some embodiments, videofront end 2934 receives pipeline commands from the command streamer2903. In some embodiments, media pipeline 2930 includes a separatecommand streamer. In some embodiments, video front-end 2934 processesmedia commands before sending the command to the media engine 2937. Insome embodiments, media engine 2937 includes thread spawningfunctionality to spawn threads for dispatch to thread execution logic2950 via thread dispatcher 2931.

In some embodiments, graphics processor 2900 includes a display engine2940. In some embodiments, display engine 2940 is external to processor2900 and couples with the graphics processor via the ring interconnect2902, or some other interconnect bus or fabric. In some embodiments,display engine 2940 includes a 2D engine 2941 and a display controller2943. In some embodiments, display engine 2940 contains special purposelogic capable of operating independently of the 3D pipeline. In someembodiments, display controller 2943 couples with a display device (notshown), which may be a system integrated display device, as in a laptopcomputer, or an external display device attached via a display deviceconnector.

In some embodiments, graphics pipeline 2920 and media pipeline 2930 areconfigurable to perform operations based on multiple graphics and mediaprogramming interfaces and are not specific to any one applicationprogramming interface (API). In some embodiments, driver software forthe graphics processor translates API calls that are specific to aparticular graphics or media library into commands that can be processedby the graphics processor. In some embodiments, support is provided forthe Open Graphics Library (OpenGL), Open Computing Language (OpenCL),and/or Vulkan graphics and compute API, all from the Khronos Group. Insome embodiments, support may also be provided for the Direct3D libraryfrom the Microsoft Corporation. In some embodiments, a combination ofthese libraries may be supported. Support may also be provided for theOpen Source Computer Vision Library (OpenCV). A future API with acompatible 3D pipeline would also be supported if a mapping can be madefrom the pipeline of the future API to the pipeline of the graphicsprocessor.

Additional Exemplary Graphics Pipeline Programming

FIG. 30A is a block diagram illustrating a graphics processor commandformat 3000 according to some embodiments. FIG. 30B is a block diagramillustrating a graphics processor command sequence 3010 according to anembodiment. The solid lined boxes in FIG. 30A illustrate the componentsthat are generally included in a graphics command while the dashed linesinclude components that are optional or that are only included in asub-set of the graphics commands. The exemplary graphics processorcommand format 3000 of FIG. 30A includes data fields to identify atarget client 3002 of the command, a command operation code (opcode)3004, and the relevant data 3006 for the command. A sub-opcode 3005 anda command size 3008 are also included in some commands.

In some embodiments, client 3002 specifies the client unit of thegraphics device that processes the command data. In some embodiments, agraphics processor command parser examines the client field of eachcommand to condition the further processing of the command and route thecommand data to the appropriate client unit. In some embodiments, thegraphics processor client units include a memory interface unit, arender unit, a 2D unit, a 3D unit, and a media unit. Each client unithas a corresponding processing pipeline that processes the commands.Once the command is received by the client unit, the client unit readsthe opcode 3004 and, if present, sub-opcode 3005 to determine theoperation to perform. The client unit performs the command usinginformation in data field 3006. For some commands, an explicit commandsize 3008 is expected to specify the size of the command. In someembodiments, the command parser automatically determines the size of atleast some of the commands based on the command opcode. In someembodiments commands are aligned via multiples of a double word.

The flow diagram in FIG. 30B shows an exemplary graphics processorcommand sequence 3010. In some embodiments, software or firmware of adata processing system that features an embodiment of a graphicsprocessor uses a version of the command sequence shown to set up,execute, and terminate a set of graphics operations. A sample commandsequence is shown and described for purposes of example only asembodiments are not limited to these specific commands or to thiscommand sequence. Moreover, the commands may be issued as batch ofcommands in a command sequence, such that the graphics processor willprocess the sequence of commands in at least partially concurrence.

In some embodiments, the graphics processor command sequence 3010 maybegin with a pipeline flush command 3012 to cause any active graphicspipeline to complete the currently pending commands for the pipeline. Insome embodiments, the 3D pipeline 3022 and the media pipeline 3024 donot operate concurrently. The pipeline flush is performed to cause theactive graphics pipeline to complete any pending commands. In responseto a pipeline flush, the command parser for the graphics processor willpause command processing until the active drawing engines completepending operations and the relevant read caches are invalidated.Optionally, any data in the render cache that is marked ‘dirty’ can beflushed to memory. In some embodiments, pipeline flush command 3012 canbe used for pipeline synchronization or before placing the graphicsprocessor into a low power state.

In some embodiments, a pipeline select command 3013 is used when acommand sequence requires the graphics processor to explicitly switchbetween pipelines. In some embodiments, a pipeline select command 3013is required only once within an execution context before issuingpipeline commands unless the context is to issue commands for bothpipelines. In some embodiments, a pipeline flush command 3012 isrequired immediately before a pipeline switch via the pipeline selectcommand 3013.

In some embodiments, a pipeline control command 3014 configures agraphics pipeline for operation and is used to program the 3D pipeline3022 and the media pipeline 3024. In some embodiments, pipeline controlcommand 3014 configures the pipeline state for the active pipeline. Inone embodiment, the pipeline control command 3014 is used for pipelinesynchronization and to clear data from one or more cache memories withinthe active pipeline before processing a batch of commands.

In some embodiments, return buffer state commands 3016 are used toconfigure a set of return buffers for the respective pipelines to writedata. Some pipeline operations require the allocation, selection, orconfiguration of one or more return buffers into which the operationswrite intermediate data during processing. In some embodiments, thegraphics processor also uses one or more return buffers to store outputdata and to perform cross thread communication. In some embodiments, thereturn buffer state 3016 includes selecting the size and number ofreturn buffers to use for a set of pipeline operations.

The remaining commands in the command sequence differ based on theactive pipeline for operations. Based on a pipeline determination 3020,the command sequence is tailored to the 3D pipeline 3022 beginning withthe 3D pipeline state 3030 or the media pipeline 3024 beginning at themedia pipeline state 3040.

The commands to configure the 3D pipeline state 3030 include 3D statesetting commands for vertex buffer state, vertex element state, constantcolor state, depth buffer state, and other state variables that are tobe configured before 3D primitive commands are processed. The values ofthese commands are determined at least in part based on the particular3D API in use. In some embodiments, 3D pipeline state 3030 commands arealso able to selectively disable or bypass certain pipeline elements ifthose elements will not be used.

In some embodiments, 3D primitive 3032 command is used to submit 3Dprimitives to be processed by the 3D pipeline. Commands and associatedparameters that are passed to the graphics processor via the 3Dprimitive 3032 command are forwarded to the vertex fetch function in thegraphics pipeline. The vertex fetch function uses the 3D primitive 3032command data to generate vertex data structures. The vertex datastructures are stored in one or more return buffers. In someembodiments, 3D primitive 3032 command is used to perform vertexoperations on 3D primitives via vertex shaders. To process vertexshaders, 3D pipeline 3022 dispatches shader execution threads tographics processor execution units.

In some embodiments, 3D pipeline 3022 is triggered via an execute 3034command or event. In some embodiments, a register write triggers commandexecution. In some embodiments execution is triggered via a ‘go’ or‘kick’ command in the command sequence. In one embodiment, commandexecution is triggered using a pipeline synchronization command to flushthe command sequence through the graphics pipeline. The 3D pipeline willperform geometry processing for the 3D primitives. Once operations arecomplete, the resulting geometric objects are rasterized and the pixelengine colors the resulting pixels. Additional commands to control pixelshading and pixel back end operations may also be included for thoseoperations.

In some embodiments, the graphics processor command sequence 3010follows the media pipeline 3024 path when performing media operations.In general, the specific use and manner of programming for the mediapipeline 3024 depends on the media or compute operations to beperformed. Specific media decode operations may be offloaded to themedia pipeline during media decode. In some embodiments, the mediapipeline can also be bypassed, and media decode can be performed inwhole or in part using resources provided by one or more general purposeprocessing cores. In one embodiment, the media pipeline also includeselements for general-purpose graphics processor unit (GPGPU) operations,where the graphics processor is used to perform SIMD vector operationsusing computational shader programs that are not explicitly related tothe rendering of graphics primitives.

In some embodiments, media pipeline 3024 is configured in a similarmanner as the 3D pipeline 3022. A set of commands to configure the mediapipeline state 3040 are dispatched or placed into a command queue beforethe media object commands 3042. In some embodiments, media pipelinestate commands 3040 include data to configure the media pipelineelements that will be used to process the media objects. This includesdata to configure the video decode and video encode logic within themedia pipeline, such as encode or decode format. In some embodiments,media pipeline state commands 3040 also support the use of one or morepointers to “indirect” state elements that contain a batch of statesettings.

In some embodiments, media object commands 3042 supply pointers to mediaobjects for processing by the media pipeline. The media objects includememory buffers containing video data to be processed. In someembodiments, all media pipeline states must be valid before issuing amedia object command 3042. Once the pipeline state is configured andmedia object commands 3042 are queued, the media pipeline 3024 istriggered via an execute command 3044 or an equivalent execute event(e.g., register write). Output from media pipeline 3024 may then be postprocessed by operations provided by the 3D pipeline 3022 or the mediapipeline 3024. In some embodiments, GPGPU operations are configured andexecuted in a similar manner as media operations.

Additional Exemplary Graphics Software Architecture

FIG. 31 illustrates exemplary graphics software architecture for a dataprocessing system 3100 according to some embodiments. In someembodiments, software architecture includes a 3D graphics application3110, an operating system 3120, and at least one processor 3130. In someembodiments, processor 3130 includes a graphics processor 3132 and oneor more general-purpose processor core(s) 3134. The graphics application3110 and operating system 3120 each execute in the system memory 3150 ofthe data processing system.

In some embodiments, 3D graphics application 3110 contains one or moreshader programs including shader instructions 3112. The shader languageinstructions may be in a high-level shader language, such as theHigh-Level Shader Language (HLSL) or the OpenGL Shader Language (GLSL).The application also includes executable instructions 3114 in a machinelanguage suitable for execution by the general-purpose processor core3134. The application also includes graphics objects 3116 defined byvertex data.

In some embodiments, operating system 3120 is a Microsoft® Windows®operating system from the Microsoft Corporation, a proprietary UNIX-likeoperating system, or an open source UNIX-like operating system using avariant of the Linux kernel. The operating system 3120 can support agraphics API 3122 such as the Direct3D API, the OpenGL API, or theVulkan API. When the Direct3D API is in use, the operating system 3120uses a front-end shader compiler 3124 to compile any shader instructions3112 in HLSL into a lower-level shader language. The compilation may bea just-in-time (JIT) compilation or the application can perform shaderpre-compilation. In some embodiments, high-level shaders are compiledinto low-level shaders during the compilation of the 3D graphicsapplication 3110. In some embodiments, the shader instructions 3112 areprovided in an intermediate form, such as a version of the StandardPortable Intermediate Representation (SPIR) used by the Vulkan API.

In some embodiments, user mode graphics driver 3126 contains a back-endshader compiler 3127 to convert the shader instructions 3112 into ahardware specific representation. When the OpenGL API is in use, shaderinstructions 3112 in the GLSL high-level language are passed to a usermode graphics driver 3126 for compilation. In some embodiments, usermode graphics driver 3126 uses operating system kernel mode functions3128 to communicate with a kernel mode graphics driver 3129. In someembodiments, kernel mode graphics driver 3129 communicates with graphicsprocessor 3132 to dispatch commands and instructions.

Additional Exemplary IP Core Implementations

One or more aspects of at least one embodiment may be implemented byrepresentative code stored on a machine-readable medium which representsand/or defines logic within an integrated circuit such as a processor.For example, the machine-readable medium may include instructions whichrepresent various logic within the processor. When read by a machine,the instructions may cause the machine to fabricate the logic to performthe techniques described herein. Such representations, known as “IPcores,” are reusable units of logic for an integrated circuit that maybe stored on a tangible, machine-readable medium as a hardware modelthat describes the structure of the integrated circuit. The hardwaremodel may be supplied to various customers or manufacturing facilities,which load the hardware model on fabrication machines that manufacturethe integrated circuit. The integrated circuit may be fabricated suchthat the circuit performs operations described in association with anyof the embodiments described herein.

FIG. 32 is a block diagram illustrating an IP core development system3200 that may be used to manufacture an integrated circuit to performoperations according to an embodiment. The IP core development system3200 may be used to generate modular, re-usable designs that can beincorporated into a larger design or used to construct an entireintegrated circuit (e.g., an SOC integrated circuit). A design facility3230 can generate a software simulation 3210 of an IP core design in ahigh-level programming language (e.g., C/C++). The software simulation3210 can be used to design, test, and verify the behavior of the IP coreusing a simulation model 3212. The simulation model 3212 may includefunctional, behavioral, and/or timing simulations. A register transferlevel (RTL) design 3215 can then be created or synthesized from thesimulation model 3212. The RTL design 3215 is an abstraction of thebehavior of the integrated circuit that models the flow of digitalsignals between hardware registers, including the associated logicperformed using the modeled digital signals. In addition to an RTLdesign 3215, lower-level designs at the logic level or transistor levelmay also be created, designed, or synthesized. Thus, the particulardetails of the initial design and simulation may vary.

The RTL design 3215 or equivalent may be further synthesized by thedesign facility into a hardware model 3220, which may be in a hardwaredescription language (HDL), or some other representation of physicaldesign data. The HDL may be further simulated or tested to verify the IPcore design. The IP core design can be stored for delivery to a 3^(rd)party fabrication facility 3265 using non-volatile memory 3240 (e.g.,hard disk, flash memory, or any non-volatile storage medium).Alternatively, the IP core design may be transmitted (e.g., via theInternet) over a wired connection 3250 or wireless connection 3260. Thefabrication facility 3265 may then fabricate an integrated circuit thatis based at least in part on the IP core design. The fabricatedintegrated circuit can be configured to perform operations in accordancewith at least one embodiment described herein.

Additional Exemplary System on a Chip Integrated Circuit

FIG. 33-35 illustrated exemplary integrated circuits and associatedgraphics processors that may be fabricated using one or more IP cores,according to various embodiments described herein. In addition to whatis illustrated, other logic and circuits may be included, includingadditional graphics processors/cores, peripheral interface controllers,or general-purpose processor cores.

FIG. 33 is a block diagram illustrating an exemplary system on a chipintegrated circuit 3300 that may be fabricated using one or more IPcores, according to an embodiment. Exemplary integrated circuit 3300includes one or more application processor(s) 3305 (e.g., CPUs), atleast one graphics processor 3310, and may additionally include an imageprocessor 3315 and/or a video processor 3320, any of which may be amodular IP core from the same or multiple different design facilities.Integrated circuit 3300 includes peripheral or bus logic including a USBcontroller 3325, UART controller 3330, an SPI/SDIO controller 3335, andan I²S/I²C controller 3340. Additionally, the integrated circuit caninclude a display device 3345 coupled to one or more of ahigh-definition multimedia interface (HDMI) controller 3350 and a mobileindustry processor interface (MIPI) display interface 3355. Storage maybe provided by a flash memory subsystem 3360 including flash memory anda flash memory controller. Memory interface may be provided via a memorycontroller 3365 for access to SDRAM or SRAM memory devices. Someintegrated circuits additionally include an embedded security engine3370.

FIG. 34 is a block diagram illustrating an exemplary graphics processor3410 of a system on a chip integrated circuit that may be fabricatedusing one or more IP cores, according to an embodiment. Graphicsprocessor 3410 can be a variant of the graphics processor 3310 of FIG.33. Graphics processor 3410 includes a vertex processor 3405 and one ormore fragment processor(s) 3415A-3415N (e.g., 3415A, 3415B, 3415C,3415D, through 3415N-1, and 3415N). Graphics processor 3410 can executedifferent shader programs via separate logic, such that the vertexprocessor 3405 is optimized to execute operations for vertex shaderprograms, while the one or more fragment processor(s) 3415A-3415Nexecute fragment (e.g., pixel) shading operations for fragment or pixelshader programs. The vertex processor 3405 performs the vertexprocessing stage of the 3D graphics pipeline and generates primitivesand vertex data. The fragment processor(s) 3415A-3415N use the primitiveand vertex data generated by the vertex processor 3405 to produce aframebuffer that is displayed on a display device. In one embodiment,the fragment processor(s) 3415A-3415N are optimized to execute fragmentshader programs as provided for in the OpenGL API, which may be used toperform similar operations as a pixel shader program as provided for inthe Direct 3D API.

Graphics processor 3410 additionally includes one or more memorymanagement units (MMUs) 3420A-3420B, cache(s) 3425A-3425B, and circuitinterconnect(s) 3430A-3430B. The one or more MMU(s) 3420A-3420B providefor virtual to physical address mapping for graphics processor 3410,including for the vertex processor 3405 and/or fragment processor(s)3415A-3415N, which may reference vertex or image/texture data stored inmemory, in addition to vertex or image/texture data stored in the one ormore cache(s) 3425A-3425B. In one embodiment the one or more MMU(s)3420A-3420B may be synchronized with other MMUs within the system,including one or more MMUs associated with the one or more applicationprocessor(s) 3305, image processor 3315, and/or video processor 3320 ofFIG. 33, such that each processor 3305-3320 can participate in a sharedor unified virtual memory system. The one or more circuitinterconnect(s) 3430A-3430B enable graphics processor 3410 to interfacewith other IP cores within the SoC, either via an internal bus of theSoC or via a direct connection, according to embodiments.

FIG. 35 is a block diagram illustrating an additional exemplary graphicsprocessor 3510 of a system on a chip integrated circuit that may befabricated using one or more IP cores, according to an embodiment.Graphics processor 3510 can be a variant of the graphics processor 3310of FIG. 33. Graphics processor 3510 includes the one or more MMU(s)3420A-3420B, caches 3425A-3425B, and circuit interconnects 3430A-3430Bof the integrated circuit 3400 of FIG. 34.

Graphics processor 3510 includes one or more shader cores 3515A-3515N(e.g., 3515A, 3515B, 3515C, 3515D, 3515E, 3515F, through 3515N-1, and3515N), which provides for a unified shader core architecture in which asingle core or type or core can execute all types of programmable shadercode, including shader program code to implement vertex shaders,fragment shaders, and/or compute shaders. The exact number of shadercores present can vary among embodiments and implementations.Additionally, graphics processor 3510 includes an inter-core taskmanager 3505, which acts as a thread dispatcher to dispatch executionthreads to one or more shader cores 3515A-3515N and a tiling unit 3518to accelerate tiling operations for tile-based rendering, in whichrendering operations for a scene are subdivided in image space, forexample to exploit local spatial coherence within a scene or to optimizeuse of internal caches.

The following clauses and/or examples pertain to specific embodiments orexamples thereof. Specifics in the examples may be used anywhere in oneor more embodiments. The various features of the different embodimentsor examples may be variously combined with some features included andothers excluded to suit a variety of different applications. Examplesmay include subject matter such as a method, means for performing actsof the method, at least one machine-readable medium includinginstructions that, when performed by a machine cause the machine toperform acts of the method, or of an apparatus or system according toembodiments and examples described herein. Various components can be ameans for performing the operations or functions described.

One embodiment provides for a graphics processing unit to performcomputations associated with a neural network, the graphics processingunit comprising compute unit including a hardware logic unit havingdynamic precision fixed-point logic; a decode unit to decode aninstruction for execution by the compute unit, the instruction to causethe compute unit to perform a matrix arithmetic operation on a set ofdynamic fixed-point tensors; and a dynamic precision manager todynamically adjust the precision of a compute operation performed by thecompute unit during the matrix arithmetic operation, the dynamicprecision manager to adjust the precision of the compute operation toprevent an arithmetic overflow.

In one embodiment, the dynamic precision fixed-point logic of thecompute unit includes an integer compute unit. The integer compute unitcan include a multiplier, an adder, and accumulator, a shifter, and aregister. The register can be configured to store a dynamic fixed-pointscale factor. In one embodiment, the instruction for execution by thecompute unit is to cause the compute unit to perform a matrix operationfor a convolution operation on input data to the neural network. Thematrix operation can include and an addition or multiplicationoperation. The matrix operation can also include a multiply andaccumulate operation. In one embodiment, the dynamic precision managercan be configured to dynamically adjust the precision of the computeoperation to prevent an arithmetic overflow of the accumulator.

One embodiment provides for a method comprising receiving a set ofdynamic fixed-point tensors; computing a right-shift value using anabsolute maximum value within the set of dynamic fixed-point tensors anda dynamic range of the set of dynamic fixed-point tensors;right-shifting data values within the set of dynamic fixed-point tensorsto prevent precision loss during computation; incrementing a sharedexponent associated with the set of dynamic fixed-point tensors; andperforming a compute operation on the set of dynamic fixed-pointtensors.

One embodiment provides for a graphics processing unit to performcomputations associated with a neural network, the graphics processingunit comprising compute unit including a hardware logic unit havingdynamic precision fixed-point logic, the compute unit to receive a setof dynamic fixed-point tensors, compute, via the dynamic precisionfixed-point logic, a right-shift value using an absolute maximum valuewithin the set of dynamic fixed-point tensors and a dynamic range of theset of dynamic fixed-point tensors, right-shift data values within theset of dynamic fixed-point tensors based on the right-shift value,increment a shared exponent associated with the set of dynamicfixed-point tensors based on the right-shift value, perform a computeoperation on the set of dynamic fixed-point tensors, and generate anoutput tensor via the compute operation on the set of dynamicfixed-point tensors.

One embodiment provides for a data processing system comprising anon-transitory machine readable medium storing instructions; and one ormore processors including at least one graphics processor, the at leastone graphics processor including a compute unit including a hardwarelogic unit having dynamic precision fixed-point logic, the compute unitto perform a matrix arithmetic operation on a set of dynamic fixed-pointtensors; and a dynamic precision manager to dynamically adjust theprecision of a compute operation performed by the compute unit duringthe matrix arithmetic operation on the set of dynamic fixed-pointtensors, the dynamic precision manager to prevent an arithmetic overflowduring the compute operation.

One embodiment provides a data processing system comprising a memory tostore a set of dynamic fixed-point tensors and one or more processorsincluding at least one graphics processor, the at least one graphicsprocessor including a compute unit including a hardware logic unithaving dynamic precision fixed-point logic. The compute unit isconfigured to receive the set of dynamic fixed-point tensors, compute,via the dynamic precision fixed-point logic, a right-shift value usingan absolute maximum value within the set of dynamic fixed-point tensorsand a dynamic range of the set of dynamic fixed-point tensors,right-shift data values within the set of dynamic fixed-point tensorsbased on the right-shift value, increment a shared exponent associatedwith the set of dynamic fixed-point tensors based on the right-shiftvalue, perform a compute operation on the set of dynamic fixed-pointtensors, and generate an output tensor via the compute operation on theset of dynamic fixed-point tensors.

The embodiments described herein refer to specific configurations ofhardware, such as application specific integrated circuits (ASICs),configured to perform certain operations or having a predeterminedfunctionality. Such electronic devices typically include a set of one ormore processors coupled to one or more other components, such as one ormore storage devices (non-transitory machine-readable storage media),user input/output devices (e.g., a keyboard, a touchscreen, and/or adisplay), and network connections. The coupling of the set of processorsand other components is typically through one or more busses and bridges(also termed as bus controllers). The storage device and signalscarrying the network traffic respectively represent one or moremachine-readable storage media and machine-readable communication media.Thus, the storage devices of a given electronic device typically storecode and/or data for execution on the set of one or more processors ofthat electronic device.

Of course, one or more parts of an embodiment may be implemented usingdifferent combinations of software, firmware, and/or hardware.Throughout this detailed description, for the purposes of explanation,numerous specific details were set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however, toone skilled in the art that the embodiments may be practiced withoutsome of these specific details. In certain instances, well-knownstructures and functions were not described in elaborate detail to avoidobscuring the inventive subject matter of the embodiments. Accordingly,the scope and spirit of the invention should be judged in terms of theclaims that follow.

What is claimed is:
 1. A graphics processing unit to performcomputations associated with a neural network, the graphics processingunit comprising: compute unit including a hardware logic unit havingdynamic precision fixed-point logic, the compute unit to: receive a setof dynamic fixed-point tensors; compute, via the dynamic precisionfixed-point logic, a right-shift value using an absolute maximum valuewithin the set of dynamic fixed-point tensors and a dynamic range of theset of dynamic fixed-point tensors; right-shift data values within theset of dynamic fixed-point tensors based on the right-shift value;increment a shared exponent associated with the set of dynamicfixed-point tensors based on the right-shift value; perform a computeoperation on the set of dynamic fixed-point tensors; and generate anoutput tensor via the compute operation on the set of dynamicfixed-point tensors.
 2. The graphics processing unit as in claim 1, thedynamic precision fixed-point logic of the compute unit including aninteger compute unit including a multiplier, an adder, an accumulator, ashifter, and a register.
 3. The graphics processing unit as in claim 2,wherein register is to store the shared exponent associated with the setof dynamic fixed-point tensors.
 4. The graphics processing unit as inclaim 1, the compute unit further to: determine whether a leading zerocount of the absolute maximum value of the output tensor is above athreshold; and adjust a precision associated with the output tensor inresponse to determining whether the leading zero count is above thethreshold.
 5. The graphics processing unit as in claim 4, wherein toadjust the precision associated with the output tensor includes toadjust a right-shift value within a right-shift counter and adjust ashared exponent for the output tensor.
 6. The graphics processing unitas in claim 4, wherein to adjust the precision associated with theoutput tensor includes to increase a right-shift value within aright-shift counter and increment a shared exponent in response todetermination that the leading zero count is above the threshold.
 7. Thegraphics processing unit as in claim 4, wherein to adjust the precisionassociated with the output tensor includes to decrease a right-shiftvalue within a right-shift counter and decrement a shared exponent inresponse to determination that the leading zero count is below thethreshold.
 8. The graphics processing unit as in claim 4, the computeunit to additionally to: perform an additional compute operation, theadditional compute operation having input including the output tensor;and adjust the precision associated with the output tensor based on theadditional compute operation.
 9. A method comprising: receiving a set ofdynamic fixed-point tensors; computing a right-shift value using anabsolute maximum value within the set of dynamic fixed-point tensors anda dynamic range of the set of dynamic fixed-point tensors;right-shifting data values within the set of dynamic fixed-pointtensors; incrementing a shared exponent associated with the set ofdynamic fixed-point tensors based on an amount of right-shift applied tothe data values within the set of dynamic fixed-point tensors; andperforming a compute operation on the set of dynamic fixed-pointtensors.
 10. The method as in claim 9, wherein performing the computeoperation on the set of dynamic fixed-point tensors includes: generatingan output tensor via the compute operation on the set of dynamicfixed-point tensors; determining whether a leading zero count of theabsolute maximum value of the output tensor is above a threshold; andadjusting a precision associated with the output tensor in response todetermining whether the leading zero count is above the threshold. 11.The method as in claim 10, wherein adjusting the precision associatedwith the output tensor includes adjusting a right-shift counter andshared exponent for the output tensor.
 12. The method as in claim 10,wherein adjusting the precision associated with the output tensorincludes increasing a right-shift counter and incrementing a sharedexponent in response to determining that the leading zero count is abovethe threshold.
 13. The method as in claim 10, wherein adjusting theprecision associated with the output tensor includes decreasing aright-shift counter and decrementing a shared exponent in response todetermining that the leading zero count is below the threshold.
 14. Themethod as in claim 10, additionally comprising: performing additionalcompute operations using the output tensor; and adjusting the precisionassociated with the output tensor based on the additional computeoperations.
 15. A data processing system comprising: a memory to store aset of dynamic fixed-point tensors; and one or more processors includingat least one graphics processor, the at least one graphics processorincluding a compute unit including a hardware logic unit having dynamicprecision fixed-point logic, the compute unit to: receive the set ofdynamic fixed-point tensors; compute, via the dynamic precisionfixed-point logic, a right-shift value using an absolute maximum valuewithin the set of dynamic fixed-point tensors and a dynamic range of theset of dynamic fixed-point tensors; right-shift data values within theset of dynamic fixed-point tensors based on the right-shift value;increment a shared exponent associated with the set of dynamicfixed-point tensors based on the right-shift value; perform a computeoperation on the set of dynamic fixed-point tensors; and generate anoutput tensor via the compute operation on the set of dynamicfixed-point tensors.
 16. The data processing system as in claim 15, thecompute unit further to: determine whether a leading zero count of theabsolute maximum value of the output tensor is above a threshold; andadjust a precision associated with the output tensor in response todetermining whether the leading zero count is above the threshold. 17.The data processing system as in claim 16, wherein to adjust theprecision associated with the output tensor includes to adjust aright-shift value within a right-shift counter and adjust a sharedexponent for the output tensor.
 18. The data processing system as inclaim 16, wherein to adjust the precision associated with the outputtensor includes to increase a right-shift value within a right-shiftcounter and increment a shared exponent in response to determinationthat the leading zero count is above the threshold.
 19. The dataprocessing system as in claim 16, wherein to adjust the precisionassociated with the output tensor includes to decrease a right-shiftvalue within a right-shift counter and decrement a shared exponent inresponse to determination that the leading zero count is below thethreshold.
 20. The data processing system as in claim 16, the computeunit to additionally to: perform an additional compute operation, theadditional compute operation having input including the output tensor;and adjust the precision associated with the output tensor based on theadditional compute operation.